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

Showing 426450 of 10307 papers

TitleStatusHype
Deep Subdomain Adaptation Network for Image ClassificationCode1
Authorship Style Transfer with Policy OptimizationCode1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Deep Transferring QuantizationCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature SelectionCode1
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image ClassificationCode1
Adversarial Masking for Self-Supervised LearningCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
An Evaluation of Self-Supervised Pre-Training for Skin-Lesion AnalysisCode1
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 DiagnosisCode1
Automatic identification of segmentation errors for radiotherapy using geometric learningCode1
Detecting Omissions in Geographic Maps through Computer VisionCode1
Automatic Dialect Adaptation in Finnish and its Effect on Perceived CreativityCode1
Determining Chess Game State From an ImageCode1
Adversarial Self-Supervised Contrastive LearningCode1
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resourcesCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEGCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Compressing BERT: Studying the Effects of Weight Pruning on Transfer LearningCode1
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Benchmark Results

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