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 1326–1350 of 10307 papers

TitleStatusHype
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask LearningCode1
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic SegmentationCode1
Know Thyself: Transferable Visual Control Policies Through Robot-AwarenessCode1
Analysis of skin lesion images with deep learningCode1
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient LearningCode1
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific ModelsCode1
Label-Only Model Inversion Attacks via Knowledge TransferCode1
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic VehiclesCode1
Large Scale Fine-Grained Categorization and Domain-Specific Transfer LearningCode1
Chip Placement with Deep Reinforcement LearningCode1
Latent-based Diffusion Model for Long-tailed RecognitionCode1
A Data-Efficient Pan-Tumor Foundation Model for Oncology CT InterpretationCode1
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
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer LearningCode1
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CTCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
Learning Graph Embeddings for Compositional Zero-shot LearningCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation ExtractionCode1
Learning Stable Classifiers by Transferring Unstable FeaturesCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
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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