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 3401–3450 of 4240 papers

TitleStatusHype
NLDF: Neural Light Dynamic Fields for Efficient 3D Talking Head Generation—0
No Forgetting Learning: Memory-free Continual Learning—0
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models—0
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation—0
Noisy Neural Network Compression for Analog Storage Devices—0
Non-Autoregressive Sign Language Production via Knowledge Distillation—0
Non-target Divergence Hypothesis: Toward Understanding Domain Gaps in Cross-Modal Knowledge Distillation—0
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices—0
Normalized Feature Distillation for Semantic Segmentation—0
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge—0
Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation—0
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering—0
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation—0
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation—0
NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21—0
NVIDIA NeMo’s Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21—0
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM—0
NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
Object-centric Cross-modal Feature Distillation for Event-based Object Detection—0
Object-Centric Diffusion for Efficient Video Editing—0
OccludeNeRF: Geometric-aware 3D Scene Inpainting with Collaborative Score Distillation in NeRF—0
Occlusion-Robust FAU Recognition by Mining Latent Space of Masked Autoencoders—0
Offline-to-Online Knowledge Distillation for Video Instance Segmentation—0
Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal Propagation Analysis for Large Language Models—0
OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery—0
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
On Compressing U-net Using Knowledge Distillation—0
Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios—0
On-Device Constrained Self-Supervised Speech Representation Learning for Keyword Spotting via Knowledge Distillation—0
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning—0
One Category One Prompt: Dataset Distillation using Diffusion Models—0
One-Class Knowledge Distillation for Spoofing Speech Detection—0
On effects of Knowledge Distillation on Transfer Learning—0
One General Teacher for Multi-Data Multi-Task: A New Knowledge Distillation Framework for Discourse Relation Analysis—0
On Elastic Language Models—0
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes—0
On Estimating the Training Cost of Conversational Recommendation Systems—0
One-stop Training of Multiple Capacity Models—0
One Student Knows All Experts Know: From Sparse to Dense—0
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers—0
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process—0
On Generalizing Beyond Domains in Cross-Domain Continual Learning—0
On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models—0
On Knowledge Distillation for Direct Speech Translation—0
On Knowledge Distillation for Translating Erroneous Speech Transcriptions—0
On Knowledge distillation from complex networks for response prediction—0
Online Continual Learning For Visual Food Classification—0
Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation—0
Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision—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