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 2801–2850 of 4240 papers

TitleStatusHype
BEExformer: A Fast Inferencing Transformer Architecture via Binarization with Multiple Early Exits—0
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval—0
BERT Learns to Teach: Knowledge Distillation with Meta Learning—0
BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation—0
Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction—0
Beyond Classification: Knowledge Distillation using Multi-Object Impressions—0
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference—0
Bilateral Memory Consolidation for Continual Learning—0
BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions—0
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks—0
BJTU-WeChat's Systems for the WMT22 Chat Translation Task—0
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack—0
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation—0
Block-wise Intermediate Representation Training for Model Compression—0
BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation—0
BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation—0
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation—0
BoostingBERT:Integrating Multi-Class Boosting into BERT for NLP Tasks—0
Boosting Contrastive Learning with Relation Knowledge Distillation—0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation—0
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation—0
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization—0
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping—0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition—0
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models—0
Improving Neural Ranking via Lossless Knowledge Distillation—0
Towards Complementary Knowledge Distillation for Efficient Dense Image Prediction—0
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs—0
Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation—0
Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
Bridging the gap between Human Action Recognition and Online Action Detection—0
Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation—0
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation—0
Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems—0
Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation—0
Bring the Power of Diffusion Model to Defect Detection—0
Brittle Features May Help Anomaly Detection—0
BS-PLCNet 2: Two-stage Band-split Packet Loss Concealment Network with Intra-model Knowledge Distillation—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care—0
Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation—0
Building Lightweight Semantic Segmentation Models for Aerial Images Using Dual Relation Distillation—0
Building Vision-Language Models on Solid Foundations with Masked Distillation—0
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation—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