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 3301–3350 of 4240 papers

TitleStatusHype
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy AnnotationsCode1
Lifelong Twin Generative Adversarial Networks—0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations—0
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image ClassificationCode1
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation—0
Confidence Conditioned Knowledge Distillation—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation—0
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge TransferCode1
CoReD: Generalizing Fake Media Detection with Continual Representation using DistillationCode0
On The Distribution of Penultimate Activations of Classification Networks—0
Continual Contrastive Learning for Image ClassificationCode0
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model—0
Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural NetworkCode1
Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural NetworksCode0
Learning Efficient Vision Transformers via Fine-Grained Manifold DistillationCode1
Isotonic Data Augmentation for Knowledge Distillation—0
ESPnet-ST IWSLT 2021 Offline Speech Translation System—0
Revisiting Knowledge Distillation: An Inheritance and Exploration FrameworkCode0
Knowledge Distillation for Quality EstimationCode0
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data—0
Learning without Forgetting for 3D Point Cloud ObjectsCode0
Reward-Based 1-bit Compressed Federated Distillation on Blockchain—0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains—0
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalCode1
Dealing with training and test segmentation mismatch: FBK@IWSLT2021—0
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental LearningCode1
Efficient Inference via Universal LSH Kernel—0
Structured Sparse R-CNN for Direct Scene Graph GenerationCode1
Knowledge Distillation via Instance-level Sequence Learning—0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
Tree-Like Decision Distillation—0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Learning Student Networks in the WildCode2
Positive-Unlabeled Data Purification in the Wild for Object Detection—0
CapsuleRRT: Relationships-Aware Regression Tracking via Capsules—0
Space-Time Distillation for Video Super-Resolution—0
Teacher's pet: understanding and mitigating biases in distillation—0
Cross Modality Knowledge Distillation for Multi-Modal Aerial View Object ClassificationCode0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay—0
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Topology Distillation for Recommender System—0
Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge DistillationCode0
CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition—0
Context-Aware Image Inpainting with Learned Semantic PriorsCode1
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation—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