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 10076–10100 of 10307 papers

TitleStatusHype
Relative Camera Pose Estimation Using Convolutional Neural NetworksCode0
Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical PruningCode0
RelO: An Overlapping Relation Extraction Dataset and ModelCode0
Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in FinanceCode0
Representation Learning by Learning to CountCode0
Representation Learning via Consistent Assignment of Views to ClustersCode0
Representation Learning via Consistent Assignment of Views over Random PartitionsCode0
Reprogramming Distillation for Medical Foundation ModelsCode0
Uncovering the Hidden Cost of Model CompressionCode0
Residual Feature Integration is Sufficient to Prevent Negative TransferCode0
Resource Efficient 3D Convolutional Neural NetworksCode0
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy BreachesCode0
Rethinking Cooking State Recognition with Vision TransformersCode0
Self-supervised Label Augmentation via Input TransformationsCode0
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task AlignmentCode0
Rethinking Knowledge Transfer in Learning Using Privileged InformationCode0
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopyCode0
Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New ExperimentsCode0
Revisiting Hidden Representations in Transfer Learning for Medical ImagingCode0
Revisiting invariances and introducing priors in Gromov-Wasserstein distancesCode0
AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential DatasetsCode0
Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource LanguagesCode0
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case StudyCode0
Revisiting the Threat Space for Vision-based Keystroke Inference AttacksCode0
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationCode0
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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