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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2001–2050 of 4240 papers

TitleStatusHype
Efficient Machine Translation with Model Pruning and Quantization—0
LightVessel: Exploring Lightweight Coronary Artery Vessel Segmentation via Similarity Knowledge Distillation—0
INDUS: Effective and Efficient Language Models for Scientific Applications—0
Industry Scale Semi-Supervised Learning for Natural Language Understanding—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation—0
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation—0
Information-Theoretic GAN Compression with Variational Energy-based Model—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression—0
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions—0
Knowledge Distillation for Adaptive MRI Prostate Segmentation Based on Limit-Trained Multi-Teacher Models—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
Initial Classifier Weights Replay for Memoryless Class Incremental Learning—0
Knowledge distillation for fast and accurate DNA sequence correction—0
Efficient Knowledge Distillation via Curriculum Extraction—0
Efficient Knowledge Distillation of SAM for Medical Image Segmentation—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
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation—0
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation—0
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights—0
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation—0
Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System—0
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection—0
Collective Knowledge Graph Completion with Mutual Knowledge Distillation—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding—0
A Survey on Green Deep Learning—0
Efficient Inference via Universal LSH Kernel—0
Efficient Image Compression Using Advanced State Space Models—0
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision—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
Distillation-Enabled Knowledge Alignment for Generative Semantic Communications in AIGC Provisioning Tasks—0
Knowledge Distillation based Ensemble Learning for Neural Machine Translation—0
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation—0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
Bring the Power of Diffusion Model to Defect Detection—0
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—0
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking—0
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation—0
Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks—0
Show:102550
← PrevPage 41 of 85Next →
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