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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 1451–1500 of 4240 papers

TitleStatusHype
Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head—0
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models—0
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data—0
Joint Diffusion models in Continual Learning—0
Quantifying Knowledge Distillation Using Partial Information Decomposition—0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge DistillationCode0
Dynamic Textual Prompt For Rehearsal-free Lifelong Person Re-identification—0
Multi-Document Financial Question Answering using LLMs—0
Knowledge Distillation Neural Network for Predicting Car-following Behaviour of Human-driven and Autonomous Vehicles—0
Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion—0
Asterisk*: Keep it Simple—0
Mitigating Hallucination with ZeroG: An Advanced Knowledge Management Engine—0
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale—0
GazeGen: Gaze-Driven User Interaction for Visual Content Generation—0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation—0
Multimodal Commonsense Knowledge Distillation for Visual Question Answering—0
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
Centerness-based Instance-aware Knowledge Distillation with Task-wise Mutual Lifting for Object Detection on Drone Imagery—0
Training on the Test Model: Contamination in Ranking DistillationCode0
Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignment—0
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation—0
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation—0
On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance—0
Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image ClassificationCode0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking—0
The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation—0
Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic ImagingCode0
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study—0
Knowledge Distillation for Real-Time Classification of Early Media in Voice Communications—0
Unveiling Context-Aware Criteria in Self-Assessing LLMs—0
Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA—0
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models—0
Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data—0
AlignCap: Aligning Speech Emotion Captioning to Human Preferences—0
SIKeD: Self-guided Iterative Knowledge Distillation for mathematical reasoningCode0
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws—0
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion AugmentationCode0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
Towards Active Participant-Centric Vertical Federated Learning: Some Representations May Be All You Need—0
AttriPrompter: Auto-Prompting with Attribute Semantics for Zero-shot Nuclei Detection via Visual-Language Pre-trained ModelsCode0
SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior—0
CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare—0
Pre-training Distillation for Large Language Models: A Design Space Exploration—0
Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples—0
GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric LearningCode0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—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