SOTAVerified

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

Showing 11511175 of 4240 papers

TitleStatusHype
Adaptive Label Smoothing with Self-Knowledge0
Endpoints Weight Fusion for Class Incremental Semantic Segmentation0
Data Techniques For Online End-to-end Speech Recognition0
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation0
End-to-End Automatic Speech Recognition with Deep Mutual Learning0
End-to-End Speech Translation with Knowledge Distillation0
Data-Free Knowledge Transfer: A Survey0
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images0
Adaptive Knowledge Distillation for Classification of Hand Images using Explainable Vision Transformers0
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation0
All You Need in Knowledge Distillation Is a Tailored Coordinate System0
Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging0
Beyond Classification: Knowledge Distillation using Multi-Object Impressions0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation0
Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search0
Adaptive Knowledge Distillation between Text and Speech Pre-trained Models0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory0
Data-free Distillation with Degradation-prompt Diffusion for Multi-weather Image Restoration0
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval0
Data-Free Distillation of Language Model by Text-to-Text Transfer0
Dense Depth Distillation with Out-of-Distribution Simulated Images0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43Unverified