Zero-shot Learning Methods Research Articles

Instance-wise multi-view visual fusion for zero-shot learning

Zero-shot learning (ZSL) has become increasing popular in computer vision due to its ability to recognize categories unobserved in the training data. So far, most existing ZSL approaches adopt visual representations that are either derived from pretrained networks or learned using an end-to-end architecture. However, a single group of visual representations can hardly capture all features hidden in the images, yielding incomplete visual information. In numerous real-life scenarios, multi-view visual representations are often accessible which describe the instances more comprehensively and are potential for better learning performance. In this paper, we introduce an instance-wise multi-view visual fusion (IMVF) for zero-shot learning (ZSL) model. In accordance with the consensus principle, a multi-view visual-semantic mapping is created by minimizing the disparities of seen-class semantic projections on different views. Meanwhile, a straightforward linear constraint is performed on each seen-class instance to adhere to the complementary principle so that the cross-view information exchange is well motivated. In order to mitigate the domain shift problem, the predicted unseen-class semantic projections are further refined through a multi-view manifold alignment under the consensus principle. Our proposed IMVFZSL is compared with the State-of-the-Art ZSL methods on AwA2, CUB and SUN datasets. Exciting experimental results validate the effectiveness of the IMVF mechanism. To the best of our understanding, this is an initial attempt to fuse multi-view visual representations in ZSL, which will stimulate valuable contemplation in this field.

Comparative Study of GPT-4Vision and Convolutional Neural Networks in Histopathological Image Analysis

Abstract Introduction/Objective Recent advancements in artificial intelligence (AI), particularly with Large Language Models like ChatGPT-4Vision (GPT-4V), have expanded the potential for automated interpretation of medical images. This study evaluates the diagnostic accuracy of GPT-4V in histopathological analysis of neurodegenerative diseases and compares its performance with traditional Convolutional Neural Networks (CNNs). Methods/Case Report We utilized 1515 histopathological images, including hematoxylin and eosin (H&E) staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer’s disease, progressive supranuclear palsy, and corticobasal degeneration. GPT-4V’s performance was assessed through multi- step prompts to assess how textual context influences the image interpretation. In the quantitative analysis, the diagnostic accuracy of GPT-4V and the CNN-based model YOLOv8 was assessed for classifying three tau lesions—astrocytic plaques, neuritic plaques, and tufted astrocytes. The analysis used both zero-shot and few-shot learning methods, where GPT-4V and YOLOv8 were evaluated on their ability to distinguish these lesions using ten hold-out test images and 490 training images per lesion. Results (if a Case Study enter NA) GPT-4V accurately recognized Lewy bodies in all cases, but it showed limited accuracy in identifying neurofibrillary tangles (20%) and Pick bodies (0%) on H&E slides. Notably, GPT-4V often suggested Alzheimer’s disease as a potential diagnosis, relying more on contextual clues, such as the prevalence of certain diseases, rather than direct image analysis. In the tau immunohistochemical analysis, GPT-4V’s diagnoses were strongly influenced by additional information about staining and brain region. However, few-shot learning markedly improved GPT-4V’s diagnostic capabilities from 40% accuracy with zero-shot learning to 90% with few-shot learning, reaching best accuracy with 20-shot learning. This performance compared favorably against YOLOv8, which also achieved 90% accuracy but required 100-shot learning. Conclusion While GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its accuracy. This approach is particularly promising for neuropathology, where acquiring extensive labeled datasets is challenging. The findings highlight the need for ongoing refinement of AI applications in pathology, emphasizing balanced integration of textual and visual data to optimize diagnostic efficacy and reliability.

Preserving text space integrity for robust compositional zero-shot learning via mixture of pretrained experts

In the current landscape of Compositional Zero-Shot Learning (CZSL) methods that leverage CLIP, the predominant approach is based on prompt learning paradigms. These methods encounter significant computational complexity when dealing with a large number of categories. Additionally, when confronted with new classification tasks, there is a necessity to learn the prompts again, which can be both time-consuming and resource-intensive. To address these challenges, We present a new methodology, named the Mixture of Pretrained Expert (MoPE), for enhancing Compositional Zero-shot Learning through Logit-Level Fusion with Mutile Expert Fusion Module. The MoPE skillfully blends the benefits of extensive pre-trained models like CLIP, Bert, GPT-3 and Word2Vec for effectively tackling Compositional Zero-shot Learning. Firstly, we extract the text label space for each language model individually, then map the visual feature vectors to their respective text spaces. This maintains the integrity and structure of the original text space. During this process, the pre-trained expert parameters are kept frozen. The mapping of visual features to the corresponding text spaces is subject to learning and could be considered as multiple learnable visual experts. In the model fusion phase, we propose a new fusion strategy that features a gating mechanism that adjusts the contributions of various models dynamically. This enables our approach to adapt more effectively to a range of tasks and data sets. The method’s robustness is demonstrated by the fact that the language model is not tailored to specific downstream task datasets or losses. This preserves the larger model’s topology and expands the potential for application. Preliminary experiments conducted on the UT-Zappos, AO-Clever, and C-GQA datasets indicate that MoPE performs competitively when compared to existing techniques.

A Robust Generalized Zero-Shot Learning Method with Attribute Prototype and Discriminative Attention Mechanism

In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the generalization ability and efficiency of GZSL models by integrating a Convolutional Block Attention Module (CBAM). The CBAM blends channel-wise and spatial-wise information to emphasize key features, thereby improving the model’s discriminative and localization capabilities. Additionally, the method employs a ResNet101 backbone for systematic image feature extraction, enhanced contrastive learning, and a similarity map generator with attribute prototypes. This comprehensive framework aims to achieve robust visual–semantic embedding for classification tasks. The proposed method demonstrates significant improvements in performance metrics in benchmark datasets, showcasing its potential in advancing GZSL applications.

Effective zero-shot learning method for event classification in Φ-OTDR sensing systems

Despite various Φ-OTDR intrusion event recognition methods having achieved high average accuracy rates (over 90%), these methods usually rely on a large amount of training sample data (80% of the data). When faced with certain intrusion events that are difficult to simulate or have few samples available, the model tends to overfit common types of intrusion events. To address this issue, this paper proposes a zero-sample learning one-dimensional residual model based on attribute point loss (APL-ZSL-1DResNet) to recognize one-dimensional intrusion event signals when training samples are insufficient. The proposed method is validated on two datasets, including a self-made dataset and an open dataset. In the experiments, each category of samples was set as zero-sample intrusion events, achieving an average recall rate of 75% and 66% respectively for zero-sample events, and an average recall rate of 94.6% and 83.5% respectively for common intrusion events.

Enhancing Cross-Lingual Sarcasm Detection by a Prompt Learning Framework with Data Augmentation and Contrastive Learning

Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins.

GNDAN: Graph Navigated Dual Attention Network for Zero-Shot Learning.

Zero-shot learning (ZSL) tackles the unseen class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a direct embedding is adopted for associating the visual and semantic domains in ZSL. However, most existing ZSL methods focus on learning the embedding from implicit global features or image regions to the semantic space. Thus, they fail to: 1) exploit the appearance relationship priors between various local regions in a single image, which corresponds to the semantic information and 2) learn cooperative global and local features jointly for discriminative feature representations. In this article, we propose the novel graph navigated dual attention network (GNDAN) for ZSL to address these drawbacks. GNDAN employs a region-guided attention network (RAN) and a region-guided graph attention network (RGAT) to jointly learn a discriminative local embedding and incorporate global context for exploiting explicit global embeddings under the guidance of a graph. Specifically, RAN uses soft spatial attention to discover discriminative regions for generating local embeddings. Meanwhile, RGAT employs an attribute-based attention to obtain attribute-based region features, where each attribute focuses on the most relevant image regions. Motivated by the graph neural network (GNN), which is beneficial for structural relationship representations, RGAT further leverages a graph attention network to exploit the relationships between the attribute-based region features for explicit global embedding representations. Based on the self-calibration mechanism, the joint visual embedding learned is matched with the semantic embedding to form the final prediction. Extensive experiments on three benchmark datasets demonstrate that the proposed GNDAN achieves superior performances to the state-of-the-art methods. Our code and trained models are available at https://github.com/shiming-chen/GNDAN.

A Dynamic Learning Method towards Realistic Compositional Zero-Shot Learning

To tackle the challenge of recognizing images of unseen attribute-object compositions, Compositional Zero-Shot Learning (CZSL) methods have been previously addressed. However, test images in realistic scenarios may also incorporate other forms of unknown factors, such as novel semantic concepts or novel image styles. As previous CZSL works have overlooked this critical issue, in this research, we first propose the Realistic Compositional Zero-Shot Learning (RCZSL) task which considers the various types of unknown factors in an unified experimental setting. To achieve this, we firstly conduct re-labelling on MIT-States and use the pre-trained generative models to obtain images of various domains. Then the entire dataset is split into a training set and a test set, with the latter containing images of unseen concepts, unseen compositions, unseen domains as well as their combinations. Following this, we show that the visual-semantic relationship changes on unseen images, leading us to construct two dynamic modulators to adapt the visual features and composition prototypes in accordance with the input image. We believe that such a dynamic learning method could effectively alleviate the domain shift problem caused by various types of unknown factors. We conduct extensive experiments on benchmark datasets for both the conventional CZSL setting and the proposed RCZSL setting. The effectiveness of our method has been proven by empirical results, which significantly outperformed both our baseline method and state-of-the-art approaches.

Data-Free Generalized Zero-Shot Learning

Deep learning models have the ability to extract rich knowledge from large-scale datasets. However, the sharing of data has become increasingly challenging due to concerns regarding data copyright and privacy. Consequently, this hampers the effective transfer of knowledge from existing data to novel downstream tasks and concepts. Zero-shot learning (ZSL) approaches aim to recognize new classes by transferring semantic knowledge learned from base classes. However, traditional generative ZSL methods often require access to real images from base classes and rely on manually annotated attributes, which presents challenges in terms of data restrictions and model scalability. To this end, this paper tackles a challenging and practical problem dubbed as data-free zero-shot learning (DFZSL), where only the CLIP-based base classes data pre-trained classifier is available for zero-shot classification. Specifically, we propose a generic framework for DFZSL, which consists of three main components. Firstly, to recover the virtual features of the base data, we model the CLIP features of base class images as samples from a von Mises-Fisher (vMF) distribution based on the pre-trained classifier. Secondly, we leverage the text features of CLIP as low-cost semantic information and propose a feature-language prompt tuning (FLPT) method to further align the virtual image features and textual features. Thirdly, we train a conditional generative model using the well-aligned virtual image features and corresponding semantic text features, enabling the generation of new classes features and achieve better zero-shot generalization. Our framework has been evaluated on five commonly used benchmarks for generalized ZSL, as well as 11 benchmarks for the base-to-new ZSL. The results demonstrate the superiority and effectiveness of our approach. Our code is available in https://github.com/ylong4/DFZSL.

Target Detection and Segmentation Technology for Zero-shot Learning

Zero-shot learning (ZSL) in the field of computer vision refers to enabling the model to recognize and understand categories that have not been encountered during the training phase. It is particularly critical for object detection and segmentation tasks, because these tasks require the model to have good generalization capabilities to unknown categories. Object detection requires the model to determine the location of the object, while segmentation further requires the precise demarcation of the object's boundaries. In ZSL research, knowledge representation and transfer are core issues. Researchers have tried to use semantic attributes as a knowledge bridge to connect categories seen during the training phase and categories not seen during the testing phase. These attributes may be color, shape, etc., but this method requires accurate attribute annotation, which is often not easy to achieve in practice. Therefore, researchers have begun to explore the use of non-visual information such as knowledge maps and text descriptions to enrich the recognition capabilities of models, but this also introduces the challenge of information integration and alignment. At present, ZSL has made certain progress in target detection and segmentation tasks, but there is still a significant gap compared with traditional supervised learning. This is mainly due to the limited ability of ZSL models to generalize to new categories. To this end, researchers have begun to explore combining ZSL with other technologies, such as generative adversarial networks (GANs) and reinforcement learning, to enhance the model's detection and segmentation capabilities for new categories. Future research needs to focus on several aspects. The first is how to design a more effective knowledge representation and transfer mechanism so that the model can better utilize existing knowledge. The second step is to develop new algorithms to improve the performance of ZSL in complex environments. In addition, research should focus on how to reduce the dependence on computing resources so that the ZSL method can run effectively in resource-limited environments. In summary, the research on target detection and segmentation technology of zero-shot learning is a cutting-edge topic in the field of computer vision. Despite the challenges, with the deepening of research, we expect these technologies to contribute to improving the generalization ability and intelligence level of computer vision systems.

Adaptive Relation-Aware Network for zero-shot classification

Supervised learning-based image classification in computer vision relies on visual samples containing a large amount of labeled information. Considering that it is labor-intensive to collect and label images and construct datasets manually, Zero-Shot Learning (ZSL) achieves knowledge transfer from seen categories to unseen categories by mining auxiliary information, which reduces the dependence on labeled image samples and is one of the current research hotspots in computer vision. However, most ZSL methods fail to properly measure the relationships between classes, or do not consider the differences and similarities between classes at all. In this paper, we propose Adaptive Relation-Aware Network (ARAN), a novel ZSL approach that incorporates the improved triplet loss from deep metric learning into a VAE-based generative model, which helps to model inter-class and intra-class relationships for different classes in ZSL datasets and generate an arbitrary amount of high-quality visual features containing more discriminative information. Moreover, we validate the effectiveness and superior performance of our ARAN through experimental evaluations under ZSL and more practical GZSL settings on three popular datasets AWA2, CUB, and SUN.

Interpretable Compositional Representations for Robust Few-Shot Generalization.

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network, is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB’s life cycle, and facilitate the application of intelligent fault diagnosis system.

Explanatory Object Part Aggregation for Zero-Shot Learning.

Zero-shot learning (ZSL) aims to recognize objects from unseen classes only based on labeled images from seen classes. Most existing ZSL methods focus on optimizing feature spaces or generating visual features of unseen classes, both in conventional ZSL and generalized zero-shot learning (GZSL). However, since the learned feature spaces are suboptimal, there exists many virtual connections where visual features and semantic attributes are not corresponding to each other. To reduce virtual connections, in this paper, we propose to discover comprehensive and fine-grained object parts by building explanatory graphs based on convolutional feature maps, then aggregate object parts to train a part-net to obtain prediction results. Since the aggregated object parts contain comprehensive visual features for activating semantic attributes, the virtual connections can be reduced by a large extent. Since part-net aims to extract local fine-grained visual features, some attributes related to global structures are ignored. To take advantage of both local and global visual features, we design a feature distiller to distill local features into a master-net which aims to extract global features. The experimental results on AWA2, CUB, FLO, and SUN dataset demonstrate that our proposed method obviously outperforms the state-of-the-arts in both conventional ZSL and GZSL tasks.

ZS-VAT: Learning Unbiased Attribute Knowledge for Zero-Shot Recognition Through Visual Attribute Transformer.

In zero-shot learning (ZSL), attribute knowledge plays a vital role in transferring knowledge from seen classes to unseen classes. However, most existing ZSL methods learn biased attribute knowledge, which usually results in biased attribute prediction and a decline in zero-shot recognition performance. To solve this problem and learn unbiased attribute knowledge, we propose a visual attribute Transformer for zero-shot recognition (ZS-VAT), which is an effective and interpretable Transformer designed specifically for ZSL. In ZS-VAT, we design an attribute-head self-attention (AHSA) that is capable of learning unbiased attribute knowledge. Specifically, each attribute head in AHSA first transforms the local features into attribute-reinforced features and then accumulates the attribute knowledge from all corresponding reinforced features, reducing the mutual influence between attributes and avoiding information loss. AHSA finally preserves unbiased attribute knowledge through attribute embeddings. We also propose an attribute fusion model (AFM) that learns to recover the correct category knowledge from the attribute knowledge. In particular, AFM takes all features from AHSA as input and generates global embeddings. We carried out experiments to demonstrate that the attribute knowledge from AHSA and the category knowledge from AFM are able to assist each other. During the final semantic prediction, we combine the attribute embedding prediction (AEP) and global embedding prediction (GEP). We evaluated the proposed scheme on three benchmark datasets. ZS-VAT outperformed the state-of-the-art generalized ZSL (GZSL) methods on two datasets and achieved competitive results on the other dataset.

Low-Light Phase Retrieval With Implicit Generative Priors.

Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for applications involving radiation-sensitive samples. However, most PR methods struggle in low-dose scenarios due to high shot noise. Recent advancements in optical data acquisition setups, such as in-situ CDI, have shown promise for low-dose imaging, but they rely on a time series of measurements, making them unsuitable for single-image applications. Similarly, data-driven phase retrieval techniques are not easily adaptable to data-scarce situations. Zero-shot deep learning methods based on pre-trained and implicit generative priors have been effective in various imaging tasks but have shown limited success in PR. In this work, we propose low-dose deep image prior (LoDIP), which combines in-situ CDI with the power of implicit generative priors to address single-image low-dose phase retrieval. Quantitative evaluations demonstrate LoDIP's superior performance in this task and its applicability to real experimental scenarios.

Multi-Horizon Glucose Prediction Across Populations with Deep Domain Generalization.

Real-time continuous glucose monitoring (CGM), augmented with accurate glucose prediction, offers an effective strategy for maintaining blood glucose levels within a therapeutically appropriate range. This is particularly crucial for individuals with type 1 diabetes (T1D) who require long-term self-management. However, with extensive glycemic variability, developing a prediction algorithm applicable across diverse populations remains a significant challenge. Leveraging meta-learning for domain generalization, we propose GPFormer, a Transformer-based zero-shot learning method designed for multi-horizon glucose prediction. We developed GPFormer on the REPLACE-BG dataset, comprising 226 participants with T1D, and proceeded to evaluate its performance using three external clinical datasets with CGM data. These included the OhioT1DM dataset, a publicly available dataset including 12 T1D participants, as well as two proprietary datasets. The first proprietary dataset included 22 participants, while the second contained 45 participants, encompassing a diverse group with T1D, type 2 diabetes, and those without diabetes, including patients admitted to hospitals. These four datasets include both outpatient and inpatient settings, various intervention strategies, and demographic variability, which effectively reflect real-world scenarios of CGM usage. When compared with a group of machine learning baseline methods, GPFormer consistently demonstrated superior performance and achieved the lowest root mean square error for all the evaluated datasets up to a prediction horizon of two hours. These experimental results highlight the effectiveness and generalizability of the proposed model across a variety of populations, demonstrating its substantial potential to enhance glucose management in a wide range of practical clinical settings.

Generative Mixup Networks for Zero-Shot Learning.

Zero-shot learning casts light on lacking unseen class data by transferring knowledge from seen classes via a joint semantic space. However, the distributions of samples from seen and unseen classes are usually imbalanced. Many zero-shot learning methods fail to obtain satisfactory results in the generalized zero-shot learning task, where seen and unseen classes are all used for the test. Also, irregular structures of some classes may result in inappropriate mapping from visual features space to semantic attribute space. A novel generative mixup networks with semantic graph alignment is proposed in this article to mitigate such problems. To be specific, our model first attempts to synthesize samples conditioned with class-level semantic information as the prototype to recover the class-based feature distribution from the given semantic description. Second, the proposed model explores a mixup mechanism to augment training samples and improve the generalization ability of the model. Third, triplet gradient matching loss is developed to guarantee the class invariance to be more continuous in the latent space, and it can help the discriminator distinguish the real and fake samples. Finally, a similarity graph is constructed from semantic attributes to capture the intrinsic correlations and guides the feature generation process. Extensive experiments conducted on several zero-shot learning benchmarks from different tasks prove that the proposed model can achieve superior performance over the state-of-the-art generalized zero-shot learning.

Mutual Information Guided Diffusion for Zero-Shot Cross-Modality Medical Image Translation.

Cross-modality data translation has attracted great interest in medical image computing. Deep generative models show performance improvement in addressing related challenges. Nevertheless, as a fundamental challenge in image translation, the problem of zero-shot learning cross-modality image translation with fidelity remains unanswered. To bridge this gap, we propose a novel unsupervised zero-shot learning method called Mutual Information guided Diffusion Model, which learns to translate an unseen source image to the target modality by leveraging the inherent statistical consistency of Mutual Information between different modalities. To overcome the prohibitive high dimensional Mutual Information calculation, we propose a differentiable local-wise mutual information layer for conditioning the iterative denoising process. The Local-wise-Mutual-Information-Layer captures identical cross-modality features in the statistical domain, offering diffusion guidance without relying on direct mappings between the source and target domains. This advantage allows our method to adapt to changing source domains without the need for retraining, making it highly practical when sufficient labeled source domain data is not available. We demonstrate the superior performance of MIDiffusion in zero-shot cross-modality translation tasks through empirical comparisons with other generative models, including adversarial-based and diffusion-based models. Finally, we showcase the real-world application of MIDiffusion in 3D zero-shot learning-based cross-modality image segmentation tasks.

IFRN: Insensitive feature removal network for zero-shot mechanical fault diagnosis across fault severity

Zero-shot learning is a promising technique for diagnosing mechanical faults in complex and uncertain environments. However, when diagnosing mechanical faults across different severities using zero-shot learning, the impact of insensitive features should be minimized due to the susceptibility of zero-shot prototypes and the vulnerability of vibration signals. To accomplish this, an Insensitive Feature Removal Network (IFRN) with an entire attention mechanism (EAM) module and a denoise autoencoder module is proposed to remove insensitive features hierarchically by dividing them into two categories: common insensitive features (CIF) and private insensitive features (PIF), each with different properties depending on their corresponding sub-labels presence in mechanical faults. Concretely, EAM removes insensitive features that the classifier cannot differentiate with the help of the entire attention weight comparison by attention generation, attention comparison, and attention limitation parts. Then, the denoise autoencoder module with long short memory term (LSTM) is utilized to remove the insensitive features that are not completely removed, especially for the insensitive features that independently arise. The IFRN’s effectiveness is demonstrated through comparative experiments and ablation studies using the Case Western Reserve University (CWRU) dataset, where the experimental result shows that IFRN outperforms conventional zero-shot learning methods. Furthermore, an analysis with prototype distance and sample aggregation is presented to further justify the effectiveness of the proposed method in reducing the prototype shift and improving classification accuracy by removing insensitive features.