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 2851–2900 of 4240 papers

TitleStatusHype
The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Framework—0
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes—0
The USYD-JD Speech Translation System for IWSLT 2021—0
The USYD-JD Speech Translation System for IWSLT2021—0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022—0
Three Factors to Improve Out-of-Distribution Detection—0
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability—0
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation—0
TinyM^2Net-V3: Memory-Aware Compressed Multimodal Deep Neural Networks for Sustainable Edge Deployment—0
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models—0
TinyViT: Fast Pretraining Distillation for Small Vision Transformers—0
TIP: Typifying the Interpretability of Procedures—0
TKD: Temporal Knowledge Distillation for Active Perception—0
ToDi: Token-wise Distillation via Fine-Grained Divergence Control—0
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models—0
Tokenizing Electron Cloud in Protein-Ligand Interaction Learning—0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion—0
Topic Modeling for Maternal Health Using Reddit—0
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data—0
Topology Distillation for Recommender System—0
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation—0
torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP—0
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation—0
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach—0
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
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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