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Knowledge Distillation

Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.

Papers

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Showing 1851–1900 of 4240 papers

TitleStatusHype
Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures—0
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation—0
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability—0
P4: Towards private, personalized, and Peer-to-Peer learning—0
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning—0
Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasetsCode0
AdaGMLP: AdaBoosting GNN-to-MLP Knowledge DistillationCode0
Pre-Trained Vision-Language Models as Partial Annotators—0
Efficient Multitask Dense Predictor via BinarizationCode0
HoverFast: an accurate, high-throughput, clinically deployable nuclear segmentation tool for brightfield digital pathology images—0
Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learning—0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory—0
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation—0
Why Not Transform Chat Large Language Models to Non-English?Code0
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch—0
Active Object Detection with Knowledge Aggregation and Distillation from Large ModelsCode0
TinyM^2Net-V3: Memory-Aware Compressed Multimodal Deep Neural Networks for Sustainable Edge Deployment—0
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D—0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Stereo-Knowledge Distillation from dpMV to Dual Pixels for Light Field Video Reconstruction—0
Evolving Storytelling: Benchmarks and Methods for New Character Customization with Diffusion Models—0
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks—0
Distill-then-prune: An Efficient Compression Framework for Real-time Stereo Matching Network on Edge Devices—0
Hierarchical Selective Classification—0
Nickel and Diming Your GAN: A Dual-Method Approach to Enhancing GAN Efficiency via Knowledge Distillation—0
Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation—0
INDUS: Effective and Efficient Language Models for Scientific Applications—0
Densely Distilling Cumulative Knowledge for Continual Learning—0
Distilling Implicit Multimodal Knowledge into Large Language Models for Zero-Resource Dialogue GenerationCode0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
GLiRA: Black-Box Membership Inference Attack via Knowledge DistillationCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—0
For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name-Gender Prediction—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
Less-supervised learning with knowledge distillation for sperm morphology analysisCode0
Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management—0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation—0
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data—0
Exploring Extreme Quantization in Spiking Language Models—0
Sub-goal Distillation: A Method to Improve Small Language AgentsCode0
Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning—0
Efficient Compression of Multitask Multilingual Speech Models—0
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All datasetsImageNetCIFAR-100COCO (Common Objects in Context)COCO 2017 valPASCAL VOCKITTI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43—Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53—Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93—Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8—Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6—Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9—Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7—Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55—Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5—Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86—Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76—Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6—Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28—Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08—Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93—Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68—Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5—Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68—Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31—Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP77.16—Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP73.73—Unverified
3ADLIK-Faster (T: Faster R-CNN vit-base S: Faster R-CNN deit-small) box AP47.6—Unverified
4ADLIK-Mask (T: Mask R-CNN vit-base S: Mask R-CNN deit-small)mask AP42.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet50))AP@0.561.8—Unverified
2ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet18))AP@0.557.96—Unverified
3ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(mobilenet-v2))AP@0.555.18—Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17—Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14—Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43—Unverified