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 1101–1125 of 10307 papers

TitleStatusHype
Cooperative Self-training of Machine Reading ComprehensionCode1
MediaSum: A Large-scale Media Interview Dataset for Dialogue SummarizationCode1
EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion RecognitionCode1
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve BackbonesCode1
A Study of Face Obfuscation in ImageNetCode1
Deepfake Videos in the Wild: Analysis and DetectionCode1
Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little DataCode1
VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning ChallengesCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
Adaptive Consistency Regularization for Semi-Supervised Transfer LearningCode1
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound ClassificationCode1
Distilling Knowledge via Intermediate ClassifiersCode1
FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance SegmentationCode1
Self-Tuning for Data-Efficient Deep LearningCode1
V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software VulnerabilitiesCode1
LogME: Practical Assessment of Pre-trained Models for Transfer LearningCode1
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised LearningCode1
Meta-Transfer Learning for Low-Resource Abstractive SummarizationCode1
End-to-end lyrics Recognition with Voice to Singing Style TransferCode1
Instance Localization for Self-supervised Detection PretrainingCode1
Zero-Shot Self-Supervised Learning for MRI ReconstructionCode1
Efficient Conditional GAN Transfer with Knowledge Propagation across ClassesCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research PapersCode1
Multi-Task Reinforcement Learning with Context-based RepresentationsCode1
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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