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 426–450 of 10307 papers

TitleStatusHype
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose trackingCode1
Deep Image Harmonization by Bridging the Reality GapCode1
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation ForecastingCode1
Deep Learning Enabled Semantic Communication SystemsCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized WeightsCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image ClassificationCode1
Adversarial Masking for Self-Supervised LearningCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep LearningCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
Deep transfer operator learning for partial differential equations under conditional shiftCode1
DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String MatchingCode1
DeiT III: Revenge of the ViTCode1
Adversarial Self-Supervised Contrastive LearningCode1
Denoised Self-Augmented Learning for Social RecommendationCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Automatic identification of segmentation errors for radiotherapy using geometric learningCode1
Detecting Omissions in Geographic Maps through Computer VisionCode1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved 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