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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 1151–1200 of 4240 papers

TitleStatusHype
Data Techniques For Online End-to-end Speech Recognition—0
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics—0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data—0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
Data-Free Knowledge Transfer: A Survey—0
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis—0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
Adaptive Knowledge Distillation for Classification of Hand Images using Explainable Vision Transformers—0
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation—0
All You Need in Knowledge Distillation Is a Tailored Coordinate System—0
Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging—0
Beyond Classification: Knowledge Distillation using Multi-Object Impressions—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
Enhancing Generalization in Chain of Thought Reasoning for Smaller Models—0
Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search—0
Adaptive Knowledge Distillation between Text and Speech Pre-trained Models—0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory—0
Data-free Distillation with Degradation-prompt Diffusion for Multi-weather Image Restoration—0
Enhancing CTC-Based Visual Speech Recognition—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Alleviating Catastrophic Forgetting of Incremental Object Detection via Within-Class and Between-Class Knowledge Distillation—0
Enhancing Data-Free Adversarial Distillation with Activation Regularization and Virtual Interpolation—0
Enhancing Scalability in Recommender Systems through Lottery Ticket Hypothesis and Knowledge Distillation-based Neural Network Pruning—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
Accurate Knowledge Distillation with n-best Reranking—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Data-Efficient Ranking Distillation for Image Retrieval—0
Adaptive Instance Distillation for Object Detection in Autonomous Driving—0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction—0
Data-Driven Compression of Convolutional Neural Networks—0
Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition—0
Enhancing Chinese Multi-Label Text Classification Performance with Response-based Knowledge Distillation—0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning—0
BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation—0
Adaptive Group Robust Ensemble Knowledge Distillation—0
BERT Learns to Teach: Knowledge Distillation with Meta Learning—0
DAKD: Data Augmentation and Knowledge Distillation using Diffusion Models for SAR Oil Spill Segmentation—0
DaFKD: Domain-Aware Federated Knowledge Distillation—0
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval—0
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization—0
Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization—0
Enhanced Multimodal Representation Learning with Cross-modal KD—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