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 1551–1600 of 4240 papers

TitleStatusHype
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition—0
Low-latency Real-time Voice Conversion on CPUCode2
Boosting Summarization with Normalizing Flows and Aggressive TrainingCode0
Group Distributionally Robust Knowledge Distillation—0
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks—0
Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo LabellingCode4
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision—0
One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge DistillationCode1
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series ForecastingCode0
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis—0
Label Poisoning is All You NeedCode1
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition—0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
Discourse Structures Guided Fine-grained Propaganda IdentificationCode0
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object DetectionCode0
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation—0
Multi-label Emotion Analysis in Conversation via Multimodal Knowledge Distillation—0
Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge DistillationCode0
Understanding the Effects of Projectors in Knowledge DistillationCode1
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained ModelCode0
torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP—0
SonoSAMTrack -- Segment and Track Anything on Ultrasound Images—0
TOP-Training: Target-Oriented Pretraining for Medical Extractive Question AnsweringCode0
Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos—0
Cross-feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous DataCode0
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation—0
Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio ModelsCode2
MCC-KD: Multi-CoT Consistent Knowledge DistillationCode0
Leveraging Complementary Attention maps in vision transformers for OCT image analysis—0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation—0
GenDistiller: Distilling Pre-trained Language Models based on Generative Models—0
DistillCSE: Distilled Contrastive Learning for Sentence EmbeddingsCode0
MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation CoefficientCode1
Leveraging Knowledge Distillation for Efficient Deep Reinforcement Learning in Resource-Constrained EnvironmentsCode0
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational AgentsCode1
A Comparative Analysis of Task-Agnostic Distillation Methods for Compressing Transformer Language Models—0
Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving—0
Transport-Hub-Aware Spatial-Temporal Adaptive Graph Transformer for Traffic Flow PredictionCode1
DistillSpec: Improving Speculative Decoding via Knowledge Distillation—0
DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech TranslationCode1
Retrieve Anything To Augment Large Language Models—0
A Discrepancy Aware Framework for Robust Anomaly DetectionCode1
Distilling Efficient Vision Transformers from CNNs for Semantic Segmentation—0
Online Speculative DecodingCode1
Distillation Improves Visual Place Recognition for Low Quality ImagesCode0
Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned DataCode0
Knowledge Distillation for Anomaly Detection—0
What do larger image classifiers memorise?—0
Applying Knowledge Distillation to Improve Weed Mapping With DronesCode0
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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