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 1751–1800 of 4240 papers

TitleStatusHype
AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages—0
Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation—0
Data-Free Knowledge Transfer: A Survey—0
Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learning—0
Knowledge distillation for optimization of quantized deep neural networks—0
Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation—0
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval—0
Completely Heterogeneous Federated Learning—0
Data Techniques For Online End-to-end Speech Recognition—0
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning—0
Embedding Compression for Teacher-to-Student Knowledge Transfer—0
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks—0
Asymmetric Image Retrieval with Cross Model Compatible Ensembles—0
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
KNIFE: Distilling Reasoning Knowledge From Free-Text Rationales—0
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR—0
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection—0
VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation—0
IOR: Inversed Objects Replay for Incremental Object Detection—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels—0
Group channel pruning and spatial attention distilling for object detection—0
Improving Autoregressive NMT with Non-Autoregressive Model—0
Grouped Knowledge Distillation for Deep Face Recognition—0
Improving CLIP Robustness with Knowledge Distillation and Self-Training—0
Group-Mix SAM: Lightweight Solution for Industrial Assembly Line Applications—0
Improving Defensive Distillation using Teacher Assistant—0
Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation—0
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence—0
Improved training of binary networks for human pose estimation and image recognition—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner—0
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation—0
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss—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