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 2026–2050 of 10307 papers

TitleStatusHype
Conditional Data Synthesis Augmentation—0
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis—0
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Conditional Neural Processes for Molecules—0
A General Regularization Framework for Domain Adaptation—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
Challenges in including extra-linguistic context in pre-trained language models—0
Confidence Aware Neural Networks for Skin Cancer Detection—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
Challenges for cognitive decoding using deep learning methods—0
Confidence Estimation in Unsupervised Deep Change Vector Analysis—0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets—0
Confidence Preserving Machine for Facial Action Unit Detection—0
A PAC-Bayesian bound for Lifelong Learning—0
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer—0
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling—0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning—0
Cross-Lingual Transfer Learning for Question Answering—0
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments—0
Cross-Linguistic Examination of Machine Translation Transfer Learning—0
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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