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 2501–2550 of 4240 papers

TitleStatusHype
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
Triplet Knowledge Distillation—0
Collective Knowledge Graph Completion with Mutual Knowledge Distillation—0
On the Impact of Knowledge Distillation for Model Interpretability—0
PruMUX: Augmenting Data Multiplexing with Model CompressionCode0
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation—0
Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios—0
HARD: Hard Augmentations for Robust Distillation—0
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness—0
Just CHOP: Embarrassingly Simple LLM Compression—0
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation—0
Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding—0
Transferring Learning Trajectories of Neural Networks—0
One-stop Training of Multiple Capacity Models—0
D^2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal SummarizationCode0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study—0
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes—0
DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding—0
Understanding the Effect of Data Augmentation on Knowledge Distillation—0
Accurate Knowledge Distillation with n-best Reranking—0
Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language UnderstandingCode0
Pseudo-Label Training and Model Inertia in Neural Machine Translation—0
Catch-Up Distillation: You Only Need to Train Once for Accelerating SamplingCode0
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval—0
DQ-Whisper: Joint Distillation and Quantization for Efficient Multilingual Speech Recognition—0
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization—0
Student-friendly Knowledge Distillation—0
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario—0
Lightweight Self-Knowledge Distillation with Multi-source Information FusionCode0
Weight-Inherited Distillation for Task-Agnostic BERT CompressionCode0
Distilling Knowledge for Short-to-Long Term Trajectory Prediction—0
Soft Prompt Decoding for Multilingual Dense Retrieval—0
Improving Defensive Distillation using Teacher Assistant—0
On enhancing the robustness of Vision Transformers: Defensive DiffusionCode0
Towards Understanding and Improving Knowledge Distillation for Neural Machine TranslationCode0
Analyzing Compression Techniques for Computer Vision—0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation—0
AMTSS: An Adaptive Multi-Teacher Single-Student Knowledge Distillation Framework For Multilingual Language Inference—0
Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping—0
A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition—0
Long-Tailed Question Answering in an Open World—0
A Survey on the Robustness of Computer Vision Models against Common CorruptionsCode0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—0
DynamicKD: An Effective Knowledge Distillation via Dynamic Entropy Correction-Based Distillation for Gap Optimizing—0
SRIL: Selective Regularization for Class-Incremental Learning—0
Multi-Teacher Knowledge Distillation For Text Image Machine TranslationCode0
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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