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 2001–2050 of 4240 papers

TitleStatusHype
DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Dealing with training and test segmentation mismatch: FBK@IWSLT2021—0
Industry Scale Semi-Supervised Learning for Natural Language Understanding—0
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference—0
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation—0
DDK: Distilling Domain Knowledge for Efficient Large Language Models—0
DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation—0
Initial Classifier Weights Replay for Memoryless Class Incremental Learning—0
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation—0
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models—0
Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval—0
Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation—0
Inplace knowledge distillation with teacher assistant for improved training of flexible deep neural networks—0
In-situ animal behavior classification using knowledge distillation and fixed-point quantization—0
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks—0
Granite Embedding Models—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning—0
Gradient-Guided Knowledge Distillation for Object Detectors—0
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection—0
Data Techniques For Online End-to-end Speech Recognition—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
Gradient Adversarial Training of Neural Networks—0
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding—0
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation—0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data—0
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics—0
Adaptive Label Smoothing with Self-Knowledge—0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval—0
Interpretable discovery of new semiconductors with machine learning—0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
Interpretable Foreground Object Search As Knowledge Distillation—0
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation—0
Data-Free Knowledge Transfer: A Survey—0
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation—0
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis—0
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving—0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data—0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization—0
Beyond Classification: Knowledge Distillation using Multi-Object Impressions—0
All You Need in Knowledge Distillation Is a Tailored Coordinate System—0
Adaptive Knowledge Distillation for Classification of Hand Images using Explainable Vision Transformers—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