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 1126–1150 of 10307 papers

TitleStatusHype
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural networkCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Point-set Distances for Learning Representations of 3D Point CloudsCode1
Contrastive Embeddings for Neural ArchitecturesCode1
Functional optimal transport: map estimation and domain adaptation for functional dataCode1
Show, Attend and Distill:Knowledge Distillation via Attention-based Feature MatchingCode1
Learning Graph Embeddings for Compositional Zero-shot LearningCode1
CODE-AE: A Coherent De-confounding Autoencoder for Predicting Patient-Specific Drug Response From Cell Line TranscriptomicsCode1
Summarising Historical Text in Modern LanguagesCode1
Malware Detection Using Frequency Domain-Based Image Visualization and Deep LearningCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Drug and Disease Interpretation Learning with Biomedical Entity Representation TransformerCode1
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character RecognitionCode1
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled DataCode1
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape ReconstructionCode1
Mitigating the Position Bias of Transformer Models in Passage Re-RankingCode1
Learning Invariant Representation for Continual LearningCode1
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized LabelsCode1
Analysis of skin lesion images with deep learningCode1
RobustSleepNet: Transfer learning for automated sleep staging at scaleCode1
Transfer Learning Between Different Architectures Via Weights InjectionCode1
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac SegmentationCode1
Improving Portuguese Semantic Role Labeling with Transformers and Transfer LearningCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture SearchCode1
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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