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 1851–1900 of 4240 papers

TitleStatusHype
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence—0
Integrated Multi-Level Knowledge Distillation for Enhanced Speaker Verification—0
HoverFast: an accurate, high-throughput, clinically deployable nuclear segmentation tool for brightfield digital pathology images—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss—0
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?—0
Deep Neural Network Models Compression—0
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting—0
Compact Speaker Embedding: lrx-vector—0
How to Backdoor the Knowledge Distillation—0
Efficient Video Segmentation Models with Per-frame Inference—0
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
Efficient Verified Machine Unlearning For Distillation—0
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model—0
Amortized Noisy Channel Neural Machine Translation—0
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis—0
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training—0
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning—0
Compacting Deep Neural Networks for Internet of Things: Methods and Applications—0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models—0
Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition—0
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation—0
Compact CNN Structure Learning by Knowledge Distillation—0
HW-TSC’s Participation in the WMT 2020 News Translation Shared Task—0
HW-TSC’s Participation in the WMT 2021 Large-Scale Multilingual Translation Task—0
A Survey on Transformer Compression—0
Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices—0
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation—0
Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control—0
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning—0
A Survey on Symbolic Knowledge Distillation of Large Language Models—0
A Flexible Multi-Task Model for BERT Serving—0
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection—0
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation—0
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation—0
I^2KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understanding—0
A Survey on Recent Teacher-student Learning Studies—0
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions—0
ICD-Face: Intra-class Compactness Distillation for Face Recognition—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Batch Selection and Communication for Active Learning with Edge Labeling—0
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
If At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—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