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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 3701–3750 of 4240 papers

TitleStatusHype
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation—0
Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation—0
Information-Theoretic GAN Compression with Variational Energy-based Model—0
Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
Initial Classifier Weights Replay for Memoryless Class Incremental Learning—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—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
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection—0
Integrated Multi-Level Knowledge Distillation for Enhanced Speaker Verification—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis—0
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding—0
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models—0
Interactive Knowledge Distillation—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
Interpretable Foreground Object Search As Knowledge Distillation—0
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation—0
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving—0
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces—0
Introspective Learning by Distilling Knowledge from Online Self-explanation—0
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning—0
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models—0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking—0
IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation—0
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study—0
Is LLM the Silver Bullet to Low-Resource Languages Machine Translation?—0
Isotonic Data Augmentation for Knowledge Distillation—0
ISP Distillation—0
Iterative Dual Domain Adaptation for Neural Machine Translation—0
Iterative Graph Self-Distillation—0
Iterative Self Knowledge Distillation -- From Pothole Classification to Fine-Grained and COVID Recognition—0
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition—0
基于层间知识蒸馏的神经机器翻译(Inter-layer Knowledge Distillation for Neural Machine Translation)—0
Joint Architecture and Knowledge Distillation in CNN for Chinese Text Recognition—0
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation—0
Joint Diffusion models in Continual Learning—0
Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition—0
Joint Input and Output Coordination for Class-Incremental Learning—0
Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment—0
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation—0
Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility—0
Joint Speech Activity and Overlap Detection with Multi-Exit Architecture—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