Image Classification Datasets Research Articles

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities.

Unstructured neural network pruning algorithms have achieved impressive compression ratios. However, the resulting-typically irregular-sparse matrices hamper efficient hardware implementations, leading to additional memory usage and complex control logic that diminishes the benefits of unstructured pruning. This has spurred structured coarse-grained pruning solutions that prune entire feature maps or even layers, enabling efficient implementation at the expense of reduced flexibility. Here, we propose a flexible new pruning mechanism that facilitates pruning at different granularities (weights, kernels, and feature maps) while retaining efficient memory organization (e.g., pruning exactly k -out-of- n weights for every output neuron or pruning exactly k -out-of- n kernels for every feature map). We refer to this algorithm as dynamic probabilistic pruning (DPP). DPP leverages the Gumbel-softmax relaxation for differentiable k -out-of- n sampling, facilitating end-to-end optimization. We show that DPP achieves competitive compression ratios and classification accuracy when pruning common deep learning models trained on different benchmark datasets for image classification. Relevantly, the dynamic masking of DPP facilitates for joint optimization of pruning and weight quantization in order to even further compress the network, which we show as well. Finally, we propose novel information-theoretic metrics that show the confidence and pruning diversity of pruning masks within a layer.

Content Based Image Retrieval System Using CNN based Deep Learning Models

Digital information search archives on the internet and other platforms have been an essential part of our daily lives. With the use of huge amounts of digital images, it is paramount to develop efficient solutions to retrieve images seamlessly. Due to inadequate textual description, the task of automated image retrieval is quite complicated. Therefore, retrieval of images by analysing their visual content is an exciting task and research-worthy. Text based Image Search (TIBR) is time consuming as initially, every image has to be linked with a keyword or text to be extracted on query search. In the proposed system, an efficient algorithm for Content Based Image Retrieval (CBIR) using pre-trained CNN-based Deep Learning models to extract deep features of an image has been developed, which significantly increases the performance of the image retrieval process. Convolutional Neural Networks (CNN) has evolved as an efficient deep learning solution for the CBIR systems that work in image recognition applications. The results of retrieval are evaluated in terms of precision performance parameters. Emphasis has been given to the comparison of the efficiency of the system using two different pre-trained CNN-based Deep Learning models based on VGG16 and ResNet-50 architecture. Transfer learning enables us to develop robust and efficient working systems using pre-trained models. In the proposed system, pre-trained models have been used to derive features using a deep learning CNN network trained for a large image classification dataset ImageNet. This approach outperforms many contemporary CBIR systems. As most CBIR systems depend on query images and as most users of such systems are non-professional, we have developed a user interface for the built prototype systems. The efficiency of the CBIR systems with pre-trained models VGG16, and ResNet-50 are compared for better image retrieval and reliability. The proposed system has addressed a few existing problems in CBIR system design and developed a suitable solution with less semantic gap and better efficiency in image retrieval.

FINE-TUNING MobileNetV3 WITH DIFFERENT WEIGHT OPTIMIZATION ALGORITHMS FOR CLASSIFICATION OF DENOISED BLOOD CELL IMAGES USING CONVOLUTIONAL NEURAL NETWORK

Breast cancer remains a formidable global health concern, underscoring the urgency for advanced diagnostic methodologies. This research presents a multifaceted framework aimed at significantly enhancing breast cancer diagnosis through innovative approaches in image processing and machine learning. The proposed framework encompasses several key contributions. Firstly, a robust denoising strategy is implemented using Convolutional Neural Network encoder-decoder architecture, augmented with data augmentation techniques. This addresses the challenge of vanishing gradients through enhanced Rectified Linear Units based Convolutional Neural Network, enhancing the model's generalization capability. Subsequent to denoising, feature extraction is performed utilizing a fine-tuned MobileNetV3 model. The model's performance is optimized through Modified Rectified Linear Units and NRMSProp approaches, effectively eliminating undesired features and improving overall efficiency. Crucially, a novel feature selection process is introduced, leveraging the Artificial Hummingbird Algorithm based on Manta Ray Foraging Optimization Algorithm. This algorithm selectively identifies essential features from breast cancer images, significantly elevating classification accuracy. To validate the proposed framework, a comprehensive evaluation is conducted, comparing its performance with a hybrid of five different metaheuristic algorithms, including Marine Predators Algorithm, Tunicate Swarm Algorithm, Manta Ray Foraging Optimization algorithm, Arithmetic Optimization Algorithm, and Jelly Fish optimization algorithm. Artificial Hummingbird Algorithm based on Manta Ray Foraging Optimization Algorithm emerges as the most effective among these algorithms, showcasing superior performance. The evaluation utilized the Breast Cancer Histopathological Image Classification dataset, resulting in an impressive classification accuracy of 99.51% for the proposed model.

Class-Level Logit Perturbation.

Features, logits, and labels are the three primary data when a sample passes through a deep neural network (DNN). Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven to be useful in various deep learning approaches. For example, (adversarial) feature perturbation can improve the robustness or even generalization capability of learned models. However, limited studies have explicitly explored for the perturbation of logit vectors. This work discusses several existing methods related to class-level logit perturbation. A unified viewpoint between regular/irregular data augmentation and loss variations incurred by logit perturbation is established. A theoretical analysis is provided to illuminate why class-level logit perturbation is useful. Accordingly, new methodologies are proposed to explicitly learn to perturb logits for both the single-label and multilabel classification tasks. Meta-learning is also leveraged to determine the regular or irregular augmentation for each class. Extensive experiments on benchmark image classification datasets and their long-tail versions indicated the competitive performance of our learning method. As it only perturbs on logit, it can be used as a plug-in to fuse with any existing classification algorithms. All the codes are available at https://github.com/limengyang1992/lpl.

An Integrated Active Deep Learning Approach for Image Classification from Unlabeled Data with Minimal Supervision

The integration of active learning (AL) and deep learning (DL) presents a promising avenue for enhancing the efficiency and performance of deep learning classifiers. This article introduces an approach that seamlessly integrates AL principles into the training process of DL models to build robust image classifiers. The proposed approach employs a unique methodology to select high-confidence unlabeled data points for immediate labeling, reducing the need for human annotation and minimizing annotation costs. Specifically, by combining uncertainty sampling with the pseudo-labeling of confident data, the proposed approach expands the training set efficiently. The proposed approach uses a hybrid active deep learning model that selects the most informative data points that need labeling based on an uncertainty measure. Then, it iteratively retrains a deep neural network classifier on the newly labeled samples. The model achieves high accuracy with fewer manually labeled samples than traditional supervised deep learning by selecting the most informative samples for labeling and retraining in a loop. Experiments on various image classification datasets demonstrate that the proposed model outperforms conventional approaches in terms of classification accuracy and reduced human annotation requirements. The proposed model achieved accuracy of 98.9% and 99.3% for the Cross-Age Celebrity and Caltech Image datasets compared to the conventional approach, which achieved 92.3% and 74.3%, respectively. In summary, this work presents a promising unified active deep learning approach to minimize the human effort in manually labeling data while maximizing classification accuracy by strategically labeling only the most valuable samples for the model.

Enhancing deep feature representation in self-knowledge distillation via pyramid feature refinement

In recent years, various self-knowledge distillation approaches have been proposed to reduce the cost of training teacher networks. However, these methods often overlook the significance of deep features. To address this limitation and strengthen the capability of deep features while preserving the ability of shallow features, we propose performing Self-Knowledge Distillation via Pyramid Feature Refinement (PR-SKD). Inspired by the representation learning characteristics of deep neural networks, PR-SKD builds a cohort of sub-networks with a pyramid architecture to hierarchically transfer refined information to the target network. According to the different contributions and functions between deep and shallow feature maps, our PR-SKD fully utilizes feature information to improve deep feature representation ability without compromising the capability of shallow feature maps. Extensive experiments on various image classification datasets demonstrate the superiority of our proposed method over widely used state-of-the-art knowledge distillation methods. The code is available at: https://github.com/wo16pao/PR-SKD.

Discriminative Dictionary Pair Learning With Scale-Constrained Structured Representation for Image Classification.

The dictionary pair learning (DPL) model aims to design a synthesis dictionary and an analysis dictionary to accomplish the goal of rapid sample encoding. In this article, we propose a novel structured representation learning algorithm based on the DPL for image classification. It is referred to as discriminative DPL with scale-constrained structured representation (DPL-SCSR). The proposed DPL-SCSR utilizes the binary label matrix of dictionary atoms to project the representation into the corresponding label space of the training samples. By imposing a non-negative constraint, the learned representation adaptively approximates a block-diagonal structure. This innovative transformation is also capable of controlling the scale of the block-diagonal representation by enforcing the sum of within-class coefficients of each sample to 1, which means that the dictionary atoms of each class compete to represent the samples from the same class. This implies that the requirement of similarity preservation is considered from the perspective of the constraint on the sum of coefficients. More importantly, the DPL-SCSR does not need to design a classifier in the representation space as the label matrix of the dictionary can also be used as an efficient linear classifier. Finally, the DPL-SCSR imposes the l2,p -norm on the analysis dictionary to make the process of feature extraction more interpretable. The DPL-SCSR seamlessly incorporates the scale-constrained structured representation learning, within-class similarity preservation of representation, and the linear classifier into one regularization term, which dramatically reduces the complexity of training and parameter tuning. The experimental results on several popular image classification datasets show that our DPL-SCSR can deliver superior performance compared with the state-of-the-art (SOTA) dictionary learning methods. The MATLAB code of this article is available at https://github.com/chenzhe207/DPL-SCSR.

Dynamic feature splicing for few-shot rare disease diagnosis.

Annotated images for rare disease diagnosis are extremely hard to collect. Therefore, identifying rare diseases under a few-shot learning (FSL) setting is significant. Existing FSL methods transfer useful and global knowledge from base classes with abundant training samples to enrich features of novel classes with few training samples, but still face difficulties when being applied to medical images due to the complex lesion characteristics and large intra-class variance. In this paper, we propose a dynamic feature splicing (DNFS) framework for few-shot rare disease diagnosis. Under DNFS, both low-level features (i.e., the output of three convolutional blocks) and high-level features (i.e., the output of the last fully connected layer) of novel classes are dynamically enriched. We construct the position coherent DNFS (P-DNFS) module to perform low-level feature splicing, where a lesion-oriented Transformer is designed to detect lesion regions. Thus, novel-class channels are replaced by similar base-class channels within the detected lesion regions to achieve disease-related feature enrichment. We also devise a semantic coherent DNFS (S-DNFS) module to perform high-level feature splicing. It explores cross-image channel relations and selects base-class channels with semantic consistency for explicit knowledge transfer. Both low-level and high-level feature splicings are performed dynamically and iteratively. Consequently, abundant spliced features are generated for disease diagnosis, leading to more accurate decision boundary and improved diagnosis performance. Extensive experiments have been conducted on three medical image classification datasets. Our results suggest that the proposed DNFS achieves superior performance against state-of-the-art approaches.

Cross channel weight sharing for image classification

Hypercomplex convolutional neural networks (HCNNs) have recently been used to improve deep learning architectures due to their ability to share weights across input channels and thus improve the cohesiveness of learned representations within the layers. Much work has been done to study the effect of hypercomplex representation in CNNs. However, to date, none of these models have used fully hypercomplex architectures, meaning that some of the layers in these earlier networks used conventional real-valued calculations that did not engage cross-channel weight sharing. Here, we introduce and study full hypercomplex CNNs by ensuring all layers perform hypercomplex calculations. For the earlier HCNNs, the real-valued layers are found in: (I) in the front end of the networks, (II) in the back end of the networks, and (III) in the residual blocks of the networks. The present research examines the performance of HCNNs when the modules mentioned above are replaced with hypercomplex equivalents. These representational networks have outperformed and shown state-of-the-art results compared to previous hypercomplex models (on some specific datasets). A disadvantage of these representational networks is that they consume high computational costs. To reduce this cost, novel separable hypercomplex networks (SHNNs) are proposed. They are created by factoring a quaternion convolutional module into two consecutive separable vectormap convolutional modules. As the successive layers of deep HCNNs perform local hierarchical grouping on increasingly abstract features, these groupings are responsible for the long-distance interaction problems found in the dense layers. To handle these problems, researchers have applied attention mechanisms in hypercomplex space.This paper offers a perspective on the basic concepts of representational networks and attention-based representational networks with the focus on (1) reviewing deep full HCNNs, including hypercomplex-based fully connected dense layer; (2) analyzing hypercomplex-based separable concept; (3) describing how the hypercomplex spatial layer of residual bottleneck block is replaced with an attention layer; and (4) providing a comprehensive summary comparing the performance of recent deep HCNNs, deep convolutional neural networks, and original attention based networks. We compare the performance of the networks in terms of accuracy and the number of trainable parameters on several image classification datasets.

SAR-HUB: Pre-Training, Fine-Tuning, and Explaining

Since the current remote sensing pre-trained models trained on optical images are not as effective when applied to SAR image tasks, it is crucial to create sensor-specific SAR models with generalized feature representations and to demonstrate with evidence the limitations of optical pre-trained models in downstream SAR tasks. The following aspects are the focus of this study: pre-training, fine-tuning, and explaining. First, we collect the current large-scale open-source SAR scene image classification datasets to pre-train a series of deep neural networks, including convolutional neural networks (CNNs) and vision transformers (ViT). A novel dynamic range adaptive enhancement method and a mini-batch class-balanced loss are proposed to tackle the challenges in SAR scene image classification. Second, the pre-trained models are transferred to various SAR downstream tasks compared with optical ones. Lastly, we propose a novel knowledge point interpretation method to reveal the benefits of the SAR pre-trained model with comprehensive and quantifiable explanations. This study is reproducible using open-source code and datasets, demonstrates generalization through extensive experiments on a variety of tasks, and is interpretable through qualitative and quantitative analyses. The codes and models are open source.

SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification

Medical image classification presents significant challenges due to limited labeled samples and class imbalance resulting from varying disease prevalence. In many real-world scenarios, a scarcity of labeled images pertaining to specific ailments coexists with an abundance of unlabeled ones. The process of annotating these unlabeled medical images by domain experts is both time-intensive and financially burdensome. Consequently, medical image classification remains an active and evolving research area. Semi-supervised learning (SSL) techniques hold promise in addressing these challenges by effectively leveraging both labeled and unlabeled data. However, the application of SSL to medical image classification necessitates overcoming two primary obstacles: (1) the reliable estimation of pseudo-labels for unlabeled images and (2) the mitigation of biases stemming from class imbalance. In this work, we introduce a novel semi-supervised learning approach, denoted as SPLAL, which adeptly addresses these challenges. SPLAL leverages the concept of class prototypes and employs a weighted combination of classifiers to predict dependable pseudo-labels for a subset of unlabeled medical images. Additionally, we introduce an alignment loss term designed to alleviate model biases, particularly those arising from the overrepresentation of majority classes. This alignment loss mechanism ensures the model’s consistency in delivering the same classification output for diverse augmented images of the same disease. To evaluate the effectiveness of our proposed approach, we conducted comprehensive experiments on two publicly accessible benchmark datasets for medical image classification. Our empirical findings unequivocally demonstrate that SPLAL outperforms several state-of-the-art semi-supervised learning methods across various evaluation metrics.

RegNet: Self-Regulated Network for Image Classification.

The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the information communication of intermediate layers of blocks is ignored. To address this issue, in this brief, we propose to introduce a regulator module as a memory mechanism to extract complementary features of the intermediate layers, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional recurrent neural networks (RNNs) [e.g., convolutional long short-term memories (LSTMs) or convolutional gated recurrent units (GRUs)], which are shown to be good at extracting spatio-temporal information. We named the new regulated network as regulated residual network (RegNet). The regulator module can be easily implemented and appended to any ResNet architecture. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, squeeze-and-excitation ResNet, and other state-of-the-art architectures.

MCDAL: Maximum Classifier Discrepancy for Active Learning.

Recent state-of-the-art active learning methods have mostly leveraged generative adversarial networks (GANs) for sample acquisition; however, GAN is usually known to suffer from instability and sensitivity to hyperparameters. In contrast to these methods, in this article, we propose a novel active learning framework that we call Maximum Classifier Discrepancy for Active Learning (MCDAL) that takes the prediction discrepancies between multiple classifiers. In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them. Intuitively, the discrepancies in the auxiliary classification layers' predictions indicate the uncertainty in the prediction. In this regard, we propose a novel method to leverage the classifier discrepancies for the acquisition function for active learning. We also provide an interpretation of our idea in relation to existing GAN-based active learning methods and domain adaptation frameworks. Moreover, we empirically demonstrate the utility of our approach where the performance of our approach exceeds the state-of-the-art methods on several image classification and semantic segmentation datasets in active learning setups.

Back-propagating errors through artificial neural networks for variable selection

Variable selection is one of the most important contents of machine learning research and a large number of variable selection methods have been proposed at present. From deep learning perspective, we propose a new variable selection method based on multi-layer artificial neural networks in this paper. The method propagates errors gained form a trained network model through the network from output layer to input layer and the values gained by input layer give information about the relative contribution. The variables with big contribution value are selected, which we call neural inverse propagation (NIP). Specifically, after training a neural network, the gained errors between the actual network outputs and the desired outputs go through the network from top to down and arrive at the input layer. The absolute values gained by input units provide information about the relative importance (contribution) of input units, which is that the larger the value is, the more important the corresponding input variable is. We remove k variables corresponding to the first k minimum absolute value gained by input units to the predictive variables, to perform variable selection. The efficiency of the proposed method is illustrated through its application to classification and regression tasks on a number of synthetic datasets and a real-world image classification dataset.

Improved deep learning image classification algorithm based on Swin Transformer V2

While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependencies. However, the self-attention mechanism used in Transformers lacks a local mechanism for information exchange within specific regions. This article attempts to leverage the strengths of both Transformers and convolutional neural networks (CNNs) to enhance the Swin Transformer V2 model. By incorporating both convolutional operation and self-attention mechanism, the enhanced model combines the local information-capturing capability of CNNs and the long-range dependency-capturing ability of Transformers. The improved model enhances the extraction of local information through the introduction of the Swin Transformer Stem, inverted residual feed-forward network, and Dual-Branch Downsampling structure. Subsequently, it models global dependencies using the improved self-attention mechanism. Additionally, downsampling is applied to the attention mechanism’s Q and K to reduce computational and memory overhead. Under identical training conditions, the proposed method significantly improves classification accuracy on multiple image classification datasets, showcasing more robust generalization capabilities.

RPIFL: Reliable and Privacy-Preserving Federated Learning for the Internet of Things

Federated Learning for the Internet of Things(FL for IoT) contributes to the enhancement of security in smart cities. However, the privacy disclosure attacks within the FL for IoT framework exposes user’s local data . Although differential privacy mechanism (DP) ensures the privacy of IoT data, one crucial issue is DP affects model accuracy and reduces model reliability. Besides, DP cannot provide comprehensive privacy preservation in the IoT environment. To tackle the above problems, we propose reliable federated learning with privacy-preserving for IoT(RPIFL). Our approach comprises three key components. First, we leverage a lightweight network to hide the private data locally, then use the output to perform the next model training. Second, we propose a multi-modal fine-grained model optimization, which significantly improves the accuracy of the IoT model. Third, we implement adaptive differential privacy mechanisms tailored to dynamic privacy requirements, thereby safeguarding the confidentiality of IoT data. To empirically validate our framework, we conducted a simulation experiment on an image classification dataset. Our methodology concurrently ensures privacy protection and achieves superior accuracy compared to the baseline model with an average accuracy increase of 1.1% over the two prevailing federated privacy protection methods, coupled with an average reduction in convergence time of over 9%.

Semantic Knowledge Guided Class-Incremental Learning

Driven by practical needs, research on Class-Incremental Learning (CIL) has received more and more attentions in recent years. A technical challenge to be conquered by CIL methods is the catastrophic forgetting problem, where the model’s performance improves rapidly on new classes while deteriorates drastically on old ones. The main causes behind catastrophic forgetting include network drifts, inter-class confusions, etc. In this paper, we propose a novel CIL method that solves the catastrophic forgetting problem from two aspects. First, to solve the inter-class confusion problem, we propose a novel Semantic knOwledge gUided ciL framework (SOUL) that consists of a CNN feature extractor and a Bi-GCN (Graph Convolutional Network) classifier. In each CIL session, we use the semantic knowledge extracted from the class labels to build two inter-class relation graphs among all the encountered old and new classes. Using these two relation graphs, we develop a Bi-GCN classifier to fuse two kinds of semantic relations in a balanced way, and then to transfer the inter-class relations from semantic modality to image classification weights. The entire SOUL framework is trained end-to-end by the standard BP algorithm, which optimizes the Bi-GCN classifier and the CNN feature extractor jointly. Second, to prevent the network drift, we develop the local topology preserving strategy that divides the global topological structure of the learned feature space into a set of local topological relations, and maintains these local relations at CIL session. Experimental evaluations demonstrate the state-of-the-art performance accuracies on benchmark image classification datasets.

Nitrogen deficiency in maize: Annotated image classification dataset.

Nitrogen (N) is one of the key inputs in maize production applied in the form of fertilizers. Nitrogen deficiency during the vegetation period leads to lower yields since N is utilized in proteins and enzymes that enable important biochemical processes such as photosynthesis. Nitrogen deficiency leads to specific symptoms that eventually become visible to the naked eye during vegetation. Our hypothesis was that N deficiency can be detected from maize RGB images in parametric process such as a deep neural network. The aim of the reported dataset is to optimize the usage of N in the farmer's fields and accordingly, reduce its environmental footprint. This dataset contains 1200 images of maize canopy from field trials, annotated by an expert from an agricultural institution. The field trials included three levels of N fertilization: N0 without N fertilization, N75 with 75kg of added N fertilizer, and NFull with 136kg of added N fertilizer. For each fertilizer level, 400 plots were created with 238 different maize genotypes, resulting in a total of 1200 plots. Images were taken with a tripod mounted DSLR camera, aperture priority set to f/8 and sensor sensitivity set to ISO400. Images were taken at a 45° angle to each plot. This dataset can be useful to both researchers, data scientists and agronomists, especially in the context of emerging technologies in precision agriculture, such as robotics, 5G networks and unmanned aerial vehicle (UAV). The dataset is one of the first publicly accessible datasets of maize canopy images under different N fertilization levels and represents a valuable public resource for development of machine learning models for in-season detection of N deficiency in maize.

Comparation of Federated and Centralized Learning for Image Classification

Federated Learning (FL) is a new approach in machine learning or it can also be called collaborative learning, which is a machine learning method that includes client devices to carry out the training process, so that clients do not need to send training data to the server but directly conduct training on their respective devices. respectively. The models generated from local training will be sent to the server for further global aggregation. Therefore, FL is referred to as machine learning which can maintain the privacy of the data owner, because the data is not submitted to the server and is still stored in each client's device. In this study, a performance comparison will be carried out to prove whether the latest approach which is Federated Learning, can produce the same accuracy performance as the traditional approach, that is Centralized Machine Learning, in the case of Image Classification. The comparison of two approaches would be conducted by using the Open Source Image Classification dataset, namely MNIST. The performance of two approaches would be presented by evaluation that is Accuracy. The result shows that Federated Learning almost overcome the performance of Centralized Learning in the case of Image Classification by provided Accuracy 76%.

Transformer-BLS: An efficient learning algorithm based on multi-head attention mechanism and incremental learning algorithms

Due to its efficient model calibration given by unique incremental learning capability, broad learning system (BLS) has made impressive progress in image analytical tasks such as image classification and object detection. Inspired by this incremental remodel success, we proposed a novel transformer-BLS network to achieve a trade-off between model training speed and accuracy. Specially, we developed sub-BLS layers with the multi-head attention mechanism and combining these layers to construct a transformer-BLS network. In particular, our proposed transformer-BLS network provides four different incremental learning algorithms that enable the proposed model can realize the increments of its feature nodes, enhancement nodes, input data and sub-BLS layers, respectively, without the need of the full-weight update in this model. Furthermore, we validated the performance of our transformer-BLS network and its four incremental learning algorithms on a variety of image classification datasets. The results demonstrated that the proposed transformer-BLS maintains classification performance on both the MNIST and Fashion-MNIST datasets, while saving 2/3 of the training time. These findings imply that the proposed method has the potential in significant reducing model training complexity with this incremental remodel system, while simultaneously improving the increment learning performance of the original BLS within such contexts, especially in the classification task of some datasets.