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
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Enhanced Sparsification via Stimulative Training—0
Answering Diverse Questions via Text Attached with Key Audio-Visual CluesCode0
Cooperative Classification and Rationalization for Graph GeneralizationCode0
Attention is all you need for boosting graph convolutional neural network—0
Knowledge Distillation of Convolutional Neural Networks through Feature Map Transformation using Decision Trees—0
Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid InferenceCode0
Fine-tuning a Multiple Instance Learning Feature Extractor with Masked Context Modelling and Knowledge Distillation—0
Scene Graph Aided Radiology Report Generation—0
Attention-guided Feature Distillation for Semantic Segmentation—0
Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Privacy-preserving Fine-tuning of Large Language Models through Flatness—0
A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech RecognitionCode0
Can Small Language Models be Good Reasoners for Sequential Recommendation?—0
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-DistillationCode0
Learning to Maximize Mutual Information for Chain-of-Thought DistillationCode0
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition—0
CSE: Surface Anomaly Detection with Contrastively Selected EmbeddingCode0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power StationCode0
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement—0
Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine TranslationCode0
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework—0
Distilling Text Style Transfer With Self-Explanation From LLMs—0
Differentially Private Knowledge Distillation via Synthetic Text GenerationCode0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs—0
Weakly Supervised Monocular 3D Detection with a Single-View Image—0
MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery—0
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning—0
A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction—0
3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document UnderstandingCode0
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization—0
SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection—0
MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning—0
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification—0
SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning—0
m2mKD: Module-to-Module Knowledge Distillation for Modular TransformersCode0
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification—0
Distilling Adversarial Robustness Using Heterogeneous Teachers—0
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off—0
Practical Insights into Knowledge Distillation for Pre-Trained Models—0
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic—0
TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth EstimationCode0
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions—0
In-Distribution Consistency Regularization Improves the Generalization of Quantization-Aware Training—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