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 2301–2350 of 4240 papers

TitleStatusHype
Toward Efficient Deep Spiking Neuron Networks:A Survey On Compression—0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach—0
Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation—0
Towards a better understanding of Vector Quantized Autoencoders—0
Towards Active Participant-Centric Vertical Federated Learning: Some Representations May Be All You Need—0
Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval—0
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text—0
Towards a Unified View of Affinity-Based Knowledge Distillation—0
Towards a Universal Continuous Knowledge Base—0
Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation—0
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift—0
Towards Comparable Knowledge Distillation in Semantic Image Segmentation—0
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation—0
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation—0
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation—0
Towards Efficient Task-Driven Model Reprogramming with Foundation Models—0
Towards Explaining Autonomy with Verbalised Decision Tree States—0
Towards Expressive Speaking Style Modelling with Hierarchical Context Information for Mandarin Speech Synthesis—0
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation—0
Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation—0
Towards Full Utilization on Mask Task for Distilling PLMs into NMT—0
Towards General and Fast Video Derain via Knowledge Distillation—0
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion—0
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models—0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts—0
Towards Making Deep Transfer Learning Never Hurt—0
Towards Model Agnostic Federated Learning Using Knowledge Distillation—0
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation—0
Towards On-Board Panoptic Segmentation of Multispectral Satellite Images—0
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge—0
Towards Oracle Knowledge Distillation with Neural Architecture Search—0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation—0
Towards Robust Classification with Image Quality Assessment—0
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach—0
Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition—0
Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation—0
Towards Streaming Egocentric Action Anticipation—0
SOCRATES: Text-based Human Search and Approach using a Robot Dog—0
Towards Unconstrained 2D Pose Estimation of the Human Spine—0
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning—0
Towards Understanding Knowledge Distillation—0
Do we need Label Regularization to Fine-tune Pre-trained Language Models?—0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Toxicity Detection can be Sensitive to the Conversational Context—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