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 1901–1950 of 4240 papers

TitleStatusHype
IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment—0
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks—0
Impossible Triangle: What's Next for Pre-trained Language Models?—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation—0
Spectral Maps for Learning on Subgraphs—0
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning—0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation—0
Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery—0
HARD: Hard Augmentations for Robust Distillation—0
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model—0
BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions—0
Improved Knowledge Distillation via Adversarial Collaboration—0
AMD: Adaptive Masked Distillation for Object Detection—0
HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-Training—0
Hands-on Guidance for Distilling Object Detectors—0
Decoupled Alignment for Robust Plug-and-Play Adaptation—0
Handling Long-tailed Feature Distribution in AdderNets—0
Improve Knowledge Distillation via Label Revision and Data Selection—0
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
GVP: Generative Volumetric Primitives—0
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation—0
Improving Autoregressive NMT with Non-Autoregressive Model—0
Improving CLIP Robustness with Knowledge Distillation and Self-Training—0
Bilateral Memory Consolidation for Continual Learning—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
Guided Deep Metric Learning—0
GTCOM Neural Machine Translation Systems for WMT19—0
Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner—0
Improving De-Raining Generalization via Neural Reorganization—0
Growing Deep Neural Network Considering with Similarity between Neurons—0
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space—0
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation—0
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation—0
Improving Feature Generalizability with Multitask Learning in Class Incremental Learning—0
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning—0
Improving Generalization of Pre-trained Language Models via Stochastic Weight Averaging—0
Improving Knowledge Distillation for BERT Models: Loss Functions, Mapping Methods, and Weight Tuning—0
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients—0
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation—0
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness—0
Group-Mix SAM: Lightweight Solution for Industrial Assembly Line Applications—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