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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 3551–3600 of 4240 papers

TitleStatusHype
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation—0
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation—0
Gradient Adversarial Training of Neural Networks—0
Gradient-Guided Knowledge Distillation for Object Detectors—0
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning—0
Granite Embedding Models—0
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks—0
Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels—0
Group channel pruning and spatial attention distilling for object detection—0
Group Distributionally Robust Knowledge Distillation—0
Grouped Knowledge Distillation for Deep Face Recognition—0
Group-Mix SAM: Lightweight Solution for Industrial Assembly Line Applications—0
Growing Deep Neural Network Considering with Similarity between Neurons—0
GTCOM Neural Machine Translation Systems for WMT19—0
Guided Deep Metric Learning—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation—0
GVP: Generative Volumetric Primitives—0
Handling Long-tailed Feature Distribution in AdderNets—0
Hands-on Guidance for Distilling Object Detectors—0
HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-Training—0
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model—0
HARD: Hard Augmentations for Robust Distillation—0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation—0
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning—0
Spectral Maps for Learning on Subgraphs—0
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—0
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks—0
Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers—0
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression—0
HeteFedRec: Federated Recommender Systems with Model Heterogeneity—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Heterogeneous-Branch Collaborative Learning for Dialogue Generation—0
Heterogeneous Continual Learning—0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning—0
Heterogeneous Federated Learning Using Knowledge Codistillation—0
Heterogeneous Generative Knowledge Distillation with Masked Image Modeling—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
Hierarchical Knowledge Distillation for Dialogue Sequence Labeling—0
Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference—0
Hierarchical Selective Classification—0
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation—0
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws—0
High-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers—0
Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach—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