Knowledge Distillation Research Articles

FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning

FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning

Lightweight and efficient deep learning models for fruit detection in orchards

The accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model’s anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher’s network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters (4.3×105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4.3 \ imes 10^5$$\\end{document}), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks.

Research and Model Library Construction in Teacher-Student Learning Architectures for Knowledge Transfer

This paper summarizes and replicates multiple classical and cutting-edge knowledge transfer methods, including Factor Transfer (FT), Knowledge Distillation (KD), Deep Mutual Learning (DML), Contrastive Representation Distillation (CRD), and Born-Again Self-Distillation (BSS). Additionally, we studied three advanced knowledge transfer methods: Relational Knowledge Distillation (RKD), Similarity-Preserving (SP), and Attention-based Feature Distillation (AFD), successfully replicating an optimized version of KD, namely RKD. Based on these methods, a flexible model library was constructed in Pycharm, allowing the quick integration of multiple knowledge transfer strategies. The experimental results are visualized through a user-friendly interface, enabling intuitive comparisons of model training speed and performance across different methods. This research provides valuable insights into the challenge of building a reusable framework that efficiently integrates various knowledge transfer strategies into deep neural networks.

Real-Time Forest Fire Detection with Lightweight CNN Using Hierarchical Multi-Task Knowledge Distillation

Forest fires pose a significant threat to ecosystems, property, and human life, making their early and accurate detection crucial for effective intervention. This study presents a novel, lightweight approach to real-time forest fire detection that is optimized for resource-constrained devices like drones. The method integrates multi-task knowledge distillation, transferring knowledge from a high-performance DenseNet201 teacher model that was trained on a hierarchically structured wildfire dataset. The dataset comprised primary classes (fire vs. non-fire) and detailed subclasses that account for confounding elements such as smoke, fog, and reflections. The novelty of this approach lies in leveraging knowledge distillation to transfer the deeper insights learned by the DenseNet201 teacher model—specifically, the auxiliary task of recognizing the confounding elements responsible for false positives—into a lightweight student model, enabling it to achieve a similar robustness without the need for complex architectures. Using this distilled knowledge, we trained a MobileNetV3-based student model, which was designed to operate efficiently in real-time while maintaining a low computational overhead. To address the challenge of false positives caused by visually similar non-fire elements, we introduced the Confounding Element Specificity (CES) metric. This novel metric, made possible by the hierarchical structure of the wildfire dataset, is unique in its focus on evaluating how well the model distinguishes actual fires from the confounding elements that typically result in false positives within the negative class. The proposed approach outperformed the baseline methods—including single-task learning and direct multi-task learning—achieving a primary accuracy of 93.36%, an F1-score of 91.57%, and a higher MES score, demonstrating its enhanced robustness and reliability in diverse environmental conditions. This work bridges the gap between advanced deep learning techniques and practical, scalable solutions for environmental monitoring. Future research will focus on integrating multi-modal data and developing adaptive distillation techniques to further enhance the model’s performance in real-time applications.

Personalized blood pressure estimation using multiview fusion information of wearable physiological signals and transfer learning

Continuous blood pressure (BP) monitoring is crucial for individual health management, yet the significant inter-individual variations among patients pose challenges to achieving precision medicine. In response to this issue, we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilities. Our proposed architecture exhibits superior performance in estimating systolic blood pressure and diastolic blood pressure, with average absolute errors of 3.94 mmHg and 2.24 mmHg, respectively. These results surpass existing baseline models and align with the standards set by the British Hypertension Society, the Association for the Advancement of Medical Instrumentation, and the Institute of Electrical and Electronics Engineers for BP measurement. Additionally, this study explores a personalized model fine-tuning strategy by adjusting specific layers and incorporating individual information, presenting an optimal solution. The model's generalization ability is validated through transfer learning across databases (public and self-made). To enhance the proposed architecture's usability in wearable devices, this study employs a knowledge distillation strategy for model lightweighting, with preliminary application in our designed real-time BP estimation system. This study provides an efficient and accurate solution for personalized BP estimation, exhibiting broad potential applications.

Knowledge Distillation and Student-Teacher Learning for Weed Detection in Turf

Abstract Machine vision-based herbicide applications relying on object detection or image classification deep convolutional neural network (DCNN) demand high memory and computational resources, resulting in lengthy inference times. To tackle these challenges, this study assessed the effectiveness of three teacher models, each trained on datasets of varying sizes, including D-20k (comprising 10,000 true positive and true negative images) and D-10k (comprising 5,000 true positive and true negative images). Additionally, knowledge distillation was performed on their corresponding student models across a range of temperature settings. After the process of student-teacher learning, the parameters of all student models were reduced. ResNet18 not only achieved higher accuracy (ACC≥0.989) but also maintained higher frames per second (FPS≥742.9) under its optimal temperature condition (T=1). Overall, the results suggest that employing knowledge distillation on the machine vision models enabled accurate and reliable weed detection in turf while reducing the need for extensive computational resources, thereby facilitating real-time weed detection and contributing to the development of smart, machine vision-based sprayers.

Knowledge Distillation for Urban Tree Image Classification Based on the Diffusion Model

As urban areas continue to emerge as significant contributors to global carbon emissions, the classification of urban street trees has gained increasing importance in carbon sequestration research. By precisely identifying and categorizing different tree species, the carbon absorption capabilities of urban vegetation can be evaluated more accurately. This understanding is essential for addressing the growing environmental challenges posed by carbon emissions, as urban trees play a crucial role in absorbing carbon dioxide and mitigating the effects of climate change. In this study, a Feature Distillation Based on Diffusion Model (FDBD) framework was proposed, utilizing state-of-the-art image classification technology to enhance the accuracy of urban tree species identification. The framework utilized knowledge distillation, a process where a smaller, more efficient “student” model is trained to mimic the performance of a larger, more complex “teacher” model, significantly reducing computational demands while maintaining high accuracy. The model’s effectiveness has been validated through combination experiments with a selected backbone model, achieving promising results. This approach not only enhances the understanding of urban trees’ carbon sequestration potential but also provides crucial insights for policymakers. By facilitating precise tree classification, it empowers urban planners to implement more informed and targeted strategies for carbon reduction, ultimately promoting more sustainable urban environments.

Effective Bi-decoding networks for rail-surface defect detection by knowledge distillation

No-service rail-surface defect detection is a crucial method for assessing the quality of railroad tracks. However, the low-contrast and dark-tone characteristics of track-surface textures pose challenges to current defect-monitoring techniques. Real-time and on-site online inspections are important to ensure safe railway operation; however, most complex models for no-service inspections are difficult to deploy on mobile devices. To address these challenges and overcome the detection difficulties associated with complex scenes, we designed a knowledge distillation-based double decoding-layer refinement network (EBDNet-KD). The first decoding process is guided by a bimodal high-level semantic feature map obtained by extending the attention-based graph convolution to incrementally enhance the dual-stream features and obtain an image restoration prior. A divide-and-conquer decoder is then designed to distinguish features using different decoding layers. The prior is then used in the second decoding layer, which enables the bimodal features to interact fully and obtain the final prediction map. We introduce a knowledge distillation strategy that enables a lightweight, compact student network to learn a complex teacher network’s feature extraction process. This facilitates pixel-consistent learning of the knowledge within the bi-decoder layer, as well as bidirectional learning of the focused contextual response knowledge to optimize the model. The EBDNet-KD significantly reduces computational costs while guaranteeing performance with a parameter count of only 28 M. EBDNet-KD demonstrated superior performance over 15 state-of-the-art methods in experiments conducted on NEU RSDDS-AUG, an industrial RGB-depth dataset. We assessed the generalizability of EBDNet-KD by evaluating its performance on three additional public datasets, yielding competitive results. The source code and results can be found at https://github.com/Wuyue15/EBDNet.

Two-stage model fusion scheme based on knowledge distillation for stragglers in federated learning

Two-stage model fusion scheme based on knowledge distillation for stragglers in federated learning

Lightweight Implementation of the Signal Enhancement Model for Early Wood-Boring Pest Monitoring

Wood-boring pests are one of the most destructive forest pests. However, the early detection of wood-boring pests is extremely difficult because their larvae live in tree trunks and have high invisibility. Borehole listening technology is a new and effective method to detect the larvae of insect pests. It identifies infested trees by analyzing wood-boring vibration signals. However, the collected wood-boring vibration signals are often disturbed by various noises existing in the field environment, which reduces the accuracy of pest detection. Therefore, it is necessary to filter out the noise and enhance the wood-boring vibration signals to facilitate the subsequent identification of pests. The current signal enhancement models are all designed based on deep learning models, which have complex scales, a large number of parameters, high demands for storage resources, large computational complexity, and high time costs. They often run on resource-rich computers or servers, and they are difficult to deploy to resource-limited field environments to realize the real-time monitoring of pests; as well, they have low practicability. Therefore, this study designs and implements two model lightweight optimization algorithms, one is a pre-training pruning algorithm based on masks, and the other is a knowledge distillation algorithm based on the separate transfer of vibration signal knowledge and noise signal knowledge. We apply the two lightweight optimization algorithms to the signal enhancement model T-CENV with good performance outcomes and conduct a series of ablation experiments. The experimental results show that the proposed methods effectively reduce the volume of the T-CENV model, which make them useful for the deployment of signal enhancement models on embedded devices, improve the usability of the model, and help to realize the real-time monitoring of wood-boring pest larvae.

CellSAM: Advancing Pathologic Image Cell Segmentation via Asymmetric Large-Scale Vision Model Feature Distillation Aggregation Network.

Segment anything model (SAM) has attracted extensive interest as a potent large-scale image segmentation model, with prior efforts adapting it for use in medical imaging. However, the precise segmentation of cell nucleus instances remains a formidable challenge in computational pathology, given substantial morphological variations and the dense clustering of nuclei with unclear boundaries. This study presents an innovative cell segmentation algorithm named CellSAM. CellSAM has the potential to improve the effectiveness and precision of disease identification and therapy planning. As a variant of SAM, CellSAM integrates dual-image encoders and employs techniques such as knowledge distillation and mask fusion. This innovative model exhibits promising capabilities in capturing intricate cell structures and ensuring adaptability in resource-constrained scenarios. The experimental results indicate that this structure effectively enhances the quality and precision of cell segmentation. Remarkably, CellSAM demonstrates outstanding results even with minimal training data. In the evaluation of particular cell segmentation tasks, extensive comparative analyzes show that CellSAM outperforms both general fundamental models and state-of-the-art (SOTA) task-specific models. Comprehensive evaluation metrics yield scores of 0.884, 0.876, and 0.768 for mean accuracy, recall, and precision respectively. Extensive experiments show that CellSAM excels in capturing subtle details and complex structures and is capable of segmenting cells in images accurately. Additionally, CellSAM demonstrates excellent performance on clinical data, indicating its potential for robust applications in treatment planning and disease diagnosis, thereby further improving the efficiency of computer-aided medicine.

A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities.

This paper was motivated by the challenge of early AD diagnosis, particularly in scenarios when clinicians encounter varied availability of patient imaging data, such as MRI and PET scans, often constrained by cost or accessibility issues. We propose an incomplete multimodal learning framework that produces tailored models for patients with only MRI and patients with both MRI and PET. This approach improves the accuracy and effectiveness of early AD diagnosis, especially when imaging resources are limited, via bi-directional knowledge transfer. We introduced a teacher model that prioritizes extracting common information between different modalities, significantly enhancing the student model's learning process. This paper includes theoretical analysis, simulation study, and realworld case study to illustrate the method's promising potential in early AD detection. However, practitioners should be mindful of the complexities involved in model tuning. Future work will focus on improving model interpretability and expanding its application. This includes developing methods to discover the key brain regions for predictions, enhancing clinical trust, and extending the framework to incorporate a broader range of imaging modalities, demographic information, and clinical data. These advancements aim to provide a more comprehensive view of patient health and improve diagnostic accuracy across various neurodegenerative diseases.

Visual Servoing for Aerial Vegetation Sampling Systems

This research describes a vision-based control strategy that employs deep learning for an aerial manipulation system developed for vegetation sampling in remote, dangerous environments. Vegetation sampling in such places presents considerable technical challenges such as equipment failures and exposure to hazardous elements. Controlling aerial manipulation in unstructured areas such as forests remains a significant challenge because of uncertainty, complex dynamics, and the possibility of collisions. To overcome these issues, we offer a new image-based visual servoing (IBVS) method that uses knowledge distillation to provide robust, accurate, and adaptive control of the aerial vegetation sampler. A convolutional neural network (CNN) from a previous study is used to detect the grasp point, giving critical feedback for the visual servoing process. The suggested method improves the precision of visual servoing for sampling by using a learning-based approach to grip point selection and camera calibration error handling. Simulation results indicate the system can track and sample tree branches with minimum error, demonstrating that it has the potential to improve the safety and efficiency of aerial vegetation sampling.

Accelerating and Compressing Transformer-Based PLMs for Enhanced Comprehension of Computer Terminology

Pretrained language models (PLMs) have significantly advanced natural language processing (NLP), establishing the "pretraining + fine-tuning" paradigm as a cornerstone approach in the field. However, the vast size and computational demands of transformer-based PLMs present challenges, particularly regarding storage efficiency and processing speed. This paper addresses these limitations by proposing a novel lightweight PLM optimized for accurately understanding domain-specific computer terminology. Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. The model is further fine-tuned using a dataset that mixes source and target languages to enhance its versatility. Comprehensive experimental evaluations demonstrate that the proposed approach successfully achieves a balance between model efficiency and performance, offering a scalable solution for NLP tasks involving specialized terminology.

On Elastic Language Models

Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. While their large scales ensure capacity, they also hinder deployment. Knowledge distillation offers an opportunity to compress a large language model to a small one, in order to reach a reasonable latency-performance tradeoff. However, for scenarios where the number of requests (e.g., queries submitted to a search engine) is highly variant, the static tradeoff attained by the compressed language model might not always fit. Once a model is assigned with a static tradeoff, it could be inadequate in that the latency is too high when the number of requests is large, or the performance is too low when the number of requests is small. To this end, we propose an elastic language model ( ElasticLM ) that elastically adjusts the tradeoff according to the request stream. The basic idea is to introduce a compute elasticity to the compressed language model, so that the tradeoff could vary on-the-fly along a scalable and controllable compute. Specifically, we impose an elastic structure to equip ElasticLM with compute elasticity and design an elastic optimization method to learn ElasticLM under compute elasticity. To serve ElasticLM , we apply an elastic schedule. Considering the specificity of information retrieval, we adapt ElasticLM to dense retrieval and reranking, and present an ElasticDenser and an ElasticRanker, respectively. Offline evaluation is conducted on a language understanding benchmark GLUE, and several information retrieval tasks including Natural Question, Trivia QA and MS MARCO. The results show that ElasticLM along with ElasticDenser and ElasticRanker can perform correctly and competitively compared with an array of static baselines. Furthermore, an online simulation with concurrency is also carried out. The results demonstrate that ElasticLM can provide elastic tradeoffs with respect to varying request stream.

Multiloss Joint Gradient Control Knowledge Distillation for Image Classification

Knowledge distillation (KD) techniques aim to transfer knowledge from complex teacher neural networks to simpler student networks. In this study, we propose a novel knowledge distillation method called Multiloss Joint Gradient Control Knowledge Distillation (MJKD), which functions by effectively combining feature- and logit-based knowledge distillation methods with gradient control. The proposed knowledge distillation method discretely considers the gradients of the task loss (cross-entropy loss), feature distillation loss, and logit distillation loss. The experimental results suggest that logits may contain more information and should, consequently, be assigned greater weight during the gradient update process in this work. The empirical findings on the CIFAR-100 and Tiny-ImageNet datasets indicate that MJKD generally outperforms traditional knowledge distillation methods, significantly enhancing the generalization ability and classification accuracy of student networks. For instance, MJKD achieves a 63.53% accuracy on Tiny-ImageNet for the ResNet18 MobileNetV2 pair. Furthermore, we present visualizations and analyses to explore its potential working mechanisms.

Instance-Level Scaling and Dynamic Margin-Alignment Knowledge Distillation for Remote Sensing Image Scene Classification

Instance-Level Scaling and Dynamic Margin-Alignment Knowledge Distillation for Remote Sensing Image Scene Classification

MAFIKD: A Real-Time Pest Detection Method Based on Knowledge Distillation

MAFIKD: A Real-Time Pest Detection Method Based on Knowledge Distillation

3-D Line Matching Network Based on Matching Existence Guidance and Knowledge Distillation

3-D Line Matching Network Based on Matching Existence Guidance and Knowledge Distillation

HDKD: Hybrid data-efficient knowledge distillation network for medical image classification

Vision Transformers (ViTs) have achieved significant advancement in computer vision tasks due to their powerful modeling capacity. However, their performance notably degrades when trained with insufficient data due to lack of inherent inductive biases. Distilling knowledge and inductive biases from a Convolutional Neural Network (CNN) teacher has emerged as an effective strategy for enhancing the generalization of ViTs on limited datasets. Previous approaches to Knowledge Distillation (KD) have pursued two primary paths: some focused solely on distilling the logit distribution from CNN teacher to ViT student, neglecting the rich semantic information present in intermediate features due to the structural differences between them. Others integrated feature distillation along with logit distillation, yet this introduced alignment operations that limits the amount of knowledge transferred due to mismatched architectures and increased the computational overhead. To this end, this paper presents Hybrid Data-efficient Knowledge Distillation (HDKD) paradigm which employs a CNN teacher and a hybrid student. The choice of hybrid student serves two main aspects. First, it leverages the strengths of both convolutions and transformers while sharing the convolutional structure with the teacher model. Second, this shared structure enables the direct application of feature distillation without any information loss or additional computational overhead. Additionally, we propose an efficient light-weight convolutional block named Mobile Channel-Spatial Attention (MBCSA), which serves as the primary convolutional block in both teacher and student models. Extensive experiments on two medical public datasets showcase the superiority of HDKD over other state-of-the-art models and its computational efficiency. Source code at: https://github.com/omarsherif200/HDKD.