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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 2401–2450 of 4240 papers

TitleStatusHype
BEBERT: Efficient and Robust Binary Ensemble BERTCode0
Teacher-Student Architecture for Knowledge Learning: A Survey—0
Completely Heterogeneous Federated Learning—0
Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier TransformCode2
Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing—0
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?—0
Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud AnalysisCode1
Fast DistilBERT on CPUs—0
A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG DataCode1
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation—0
Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks—0
Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT ModelsCode1
Weight Averaging: A Simple Yet Effective Method to Overcome Catastrophic Forgetting in Automatic Speech Recognition—0
Improved Feature Distillation via Projector EnsembleCode1
Li3DeTr: A LiDAR based 3D Detection Transformer—0
Long-tailed Food Classification—0
GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion EstimatesCode1
Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision—0
An Effective Deep Network for Head Pose Estimation without Keypoints—0
Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation—0
Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models—0
Geometric Knowledge Distillation: Topology Compression for Graph Neural NetworksCode1
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddingsCode1
Respecting Transfer Gap in Knowledge Distillation—0
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation—0
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
Modeling Document-level Temporal Structures for Building Temporal Dependency GraphsCode0
Distilling the Undistillable: Learning from a Nasty TeacherCode0
Performance-Efficiency Trade-Offs in Adapting Language Models to Text Classification Tasks—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Semi-supervised object detection based on single-stage detector for thighbone fracture localization—0
Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation—0
A baseline revisited: Pushing the limits of multi-segment models for context-aware translation—0
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection—0
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image RetrievalCode1
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLPCode0
On effects of Knowledge Distillation on Transfer Learning—0
Distilling Object Detectors With Global KnowledgeCode0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging—0
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive PruningCode1
Learning Generalizable Models for Vehicle Routing Problems via Knowledge DistillationCode1
Improving generalizability of distilled self-supervised speech processing models under distorted settingsCode0
Knowledge Distillation approach towards Melanoma DetectionCode0
You Can Have Your Data and Balance It Too: Towards Balanced and Efficient Multilingual Models—0
Probabilistic Integration of Object Level Annotations in Chest X-ray Classification—0
Efficient Knowledge Distillation from Model CheckpointsCode1
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Integrating Translation Memories into Non-Autoregressive Machine TranslationCode0
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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