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 3151–3200 of 4240 papers

TitleStatusHype
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Distilling HuBERT with LSTMs via Decoupled Knowledge Distillation—0
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression—0
Distilling Inter-Class Distance for Semantic Segmentation—0
Distilling Invariant Representations with Dual Augmentation—0
Distilling Knowledge for Short-to-Long Term Trajectory Prediction—0
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition—0
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain—0
Distilling Knowledge from Heterogeneous Architectures for Semantic Segmentation—0
Distilling Knowledge from Pre-trained Language Models via Text Smoothing—0
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach—0
Distilling Knowledge into Quantum Vision Transformers for Biomedical Image Classification—0
Distilling Large Language Models for Efficient Clinical Information Extraction—0
Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification—0
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
Distilling Normalizing Flows—0
Distilling Object Detectors with Task Adaptive Regularization—0
Distilling ODE Solvers of Diffusion Models into Smaller Steps—0
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces—0
Distilling Pixel-Wise Feature Similarities for Semantic Segmentation—0
Distilling portable Generative Adversarial Networks for Image Translation—0
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport—0
Distilling Spatially-Heterogeneous Distortion Perception for Blind Image Quality Assessment—0
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection—0
Distilling Text Style Transfer With Self-Explanation From LLMs—0
Distilling the Knowledge in Data Pruning—0
Distilling BERT into Simple Neural Networks with Unlabeled Transfer Data—0
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification—0
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models—0
DistillSpec: Improving Speculative Decoding via Knowledge Distillation—0
Distill-then-prune: An Efficient Compression Framework for Real-time Stereo Matching Network on Edge Devices—0
Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation—0
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation—0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model—0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Distributed Learning for Wi-Fi AP Load Prediction—0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Diverse Knowledge Distillation for End-to-End Person Search—0
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks—0
DLIP: Distilling Language-Image Pre-training—0
DL-KDD: Dual-Light Knowledge Distillation for Action Recognition in the Dark—0
DMKD: Improving Feature-based Knowledge Distillation for Object Detection Via Dual Masking Augmentation—0
DNA 1.0 Technical Report—0
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models—0
Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?—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