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

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Showing 326–350 of 4240 papers

TitleStatusHype
Progressive Collaborative and Semantic Knowledge Fusion for Generative Recommendation—0
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
Rationalization Models for Text-to-SQL—0
Contrastive Representation Distillation via Multi-Scale Feature Decoupling—0
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation ModelsCode1
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Data—0
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector—0
Event Stream-based Visual Object Tracking: HDETrack V2 and A High-Definition BenchmarkCode2
Multilingual Non-Autoregressive Machine Translation without Knowledge DistillationCode0
BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation—0
Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn't Matter (Much)—0
Towards Unified Music Emotion Recognition across Dimensional and Categorical ModelsCode1
A Unified Knowledge-Distillation and Semi-Supervised Learning Framework to Improve Industrial Ads Delivery Systems—0
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
VLM-Assisted Continual learning for Visual Question Answering in Self-Driving—0
A method for estimating forest carbon storage distribution density via artificial intelligence generated content model—0
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy DistillationCode0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design—0
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems—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