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 876–900 of 10307 papers

TitleStatusHype
LEAD: Learning Decomposition for Source-free Universal Domain AdaptationCode1
Learning Bounds for Open-Set LearningCode1
Learning Causal Representations of Single Cells via Sparse Mechanism Shift ModelingCode1
Learning A Single Network for Scale-Arbitrary Super-ResolutionCode1
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation LearningCode1
Learning Generalizable Physiological Representations from Large-scale Wearable DataCode1
Learning Graph Embeddings for Compositional Zero-shot LearningCode1
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation ExtractionCode1
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
Learning to Adapt to Evolving DomainsCode1
Learning to Discover Novel Visual Categories via Deep Transfer ClusteringCode1
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate DomainsCode1
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask TrainingCode1
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
Learning Visual Representations for Transfer Learning by Suppressing TextureCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data AugmentationCode1
Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease ClassificationCode1
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual RecognitionCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Leveraging Subword Embeddings for Multinational Address ParsingCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue ModelingCode1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and ResolutionCode1
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous DatasetsCode1
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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