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 2701–2750 of 4240 papers

TitleStatusHype
Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both—0
Single image calibration using knowledge distillation approaches—0
Single Snapshot Distillation for Phase Coded Mask Design in Phase Retrieval—0
Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation—0
SKDBERT: Compressing BERT via Stochastic Knowledge Distillation—0
Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch—0
SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning—0
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples—0
SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision—0
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling—0
Small Language Models are Equation Reasoners—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
Small Vision-Language Models: A Survey on Compact Architectures and Techniques—0
Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding—0
SMOC-Net: Leveraging Camera Pose for Self-Supervised Monocular Object Pose Estimation—0
Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation—0
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training—0
SNN-PAR: Energy Efficient Pedestrian Attribute Recognition via Spiking Neural Networks—0
SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos—0
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression—0
Soft Prompt Decoding for Multilingual Dense Retrieval—0
Solvable Model for Inheriting the Regularization through Knowledge Distillation—0
SonoSAMTrack -- Segment and Track Anything on Ultrasound Images—0
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model—0
Toward Student-Oriented Teacher Network Training For Knowledge Distillation—0
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation—0
Source-Target Unified Knowledge Distillation for Memory-Efficient Federated Domain Adaptation on Edge Devices—0
Space-Time Distillation for Video Super-Resolution—0
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm—0
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm—0
Spatial Knowledge Distillation to aid Visual Reasoning—0
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection—0
Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading—0
Spatio-Temporal Graph for Video Captioning with Knowledge Distillation—0
Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency—0
Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling—0
Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation—0
Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations—0
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23—0
Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection—0
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer—0
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data—0
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions—0
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection—0
SRIL: Selective Regularization for Class-Incremental Learning—0
SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification—0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection—0
SSR: Enhancing Depth Perception in Vision-Language Models via Rationale-Guided Spatial Reasoning—0
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders—0
Static Word Embeddings for Sentence Semantic Representation—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