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

Showing 51100 of 4240 papers

TitleStatusHype
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language ModelsCode2
MobileFaceSwap: A Lightweight Framework for Video Face SwappingCode2
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image SegmentationCode2
LightGen: Efficient Image Generation through Knowledge Distillation and Direct Preference OptimizationCode2
OccDepth: A Depth-Aware Method for 3D Semantic Scene CompletionCode2
BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform MotionsCode2
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous DrivingCode2
A Cognitive-Based Trajectory Prediction Approach for Autonomous DrivingCode2
Knowledge distillation: A good teacher is patient and consistentCode2
Pre-trained Vision and Language Transformers Are Few-Shot Incremental LearnersCode2
Incremental Sequence Labeling: A Tale of Two ShiftsCode2
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New OutlooksCode2
Focal Loss for Dense Object DetectionCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model InferenceCode2
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object DetectionCode2
LightGNN: Simple Graph Neural Network for RecommendationCode2
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-ReviewCode2
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge DistillationCode2
EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise OptimizationCode2
Dual-Space Knowledge Distillation for Large Language ModelsCode2
DOT: A Distillation-Oriented TrainerCode2
Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio ModelsCode2
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt TuningCode2
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image SegmentationCode2
Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge DistillationCode2
Decoupled Knowledge DistillationCode2
A Comprehensive Survey on Knowledge DistillationCode2
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel BaselineCode2
Diffusion Time-step Curriculum for One Image to 3D GenerationCode2
From Instance Training to Instruction Learning: Task Adapters Generation from InstructionsCode2
Improving the Training of Rectified FlowsCode2
Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge DistillationCode2
JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation FrameworkCode2
Are Large Kernels Better Teachers than Transformers for ConvNets?Code2
A Unified Framework for 3D Scene UnderstandingCode2
MiniLLM: Knowledge Distillation of Large Language ModelsCode2
Cross-Image Relational Knowledge Distillation for Semantic SegmentationCode2
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated DataCode2
Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV TrackingCode2
Learning Occlusion-Robust Vision Transformers for Real-Time UAV TrackingCode2
Let Images Give You More:Point Cloud Cross-Modal Training for Shape AnalysisCode2
LibFewShot: A Comprehensive Library for Few-shot LearningCode2
Data-Free Knowledge Distillation for Deep Neural NetworksCode2
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity RecognitionCode2
Positive-Unlabeled Compression on the CloudCode2
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary StudyCode2
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point CloudsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43Unverified