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 18011825 of 4240 papers

TitleStatusHype
Improving Neural ODEs via Knowledge Distillation0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation0
In-Batch Negatives for Knowledge Distillation with Tightly-Coupled Teachers for Dense Retrieval0
Handling Long-tailed Feature Distribution in AdderNets0
Hands-on Guidance for Distilling Object Detectors0
HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-Training0
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection0
Active Learning for Lane Detection: A Knowledge Distillation Approach0
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation0
Improving Knowledge Distillation with Teacher's Explanation0
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss0
Compact Speaker Embedding: lrx-vector0
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling0
Efficient Video Segmentation Models with Per-frame Inference0
Efficient Verified Machine Unlearning For Distillation0
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks0
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model0
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression0
HeteFedRec: Federated Recommender Systems with Model Heterogeneity0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning0
Heterogeneous-Branch Collaborative Learning for Dialogue Generation0
Heterogeneous Continual Learning0
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding0
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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