SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 1401–1425 of 10307 papers

TitleStatusHype
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
MineGAN: effective knowledge transfer from GANs to target domains with few imagesCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue SystemsCode1
Mitigating the Position Bias of Transformer Models in Passage Re-RankingCode1
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domainsCode1
CLIP-Lite: Information Efficient Visual Representation Learning with Language SupervisionCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy SearchCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Practical One-Shot Federated Learning for Cross-Silo SettingCode1
Model-Based Reinforcement Learning with Isolated ImaginationsCode1
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse DetectionCode1
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural NetworksCode1
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel SynthesisCode1
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisCode1
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLOCode1
Modular Gaussian Processes for Transfer LearningCode1
ChrEn: Cherokee-English Machine Translation for Endangered Language RevitalizationCode1
Monte Carlo Tree Search based Space Transfer for Black-box OptimizationCode1
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion ForecastingCode1
Movement Pruning: Adaptive Sparsity by Fine-TuningCode1
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species ClassificationCode1
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI DataCode1
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer LearningCode1
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified