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 1451–1475 of 10307 papers

TitleStatusHype
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
Head2Toe: Utilizing Intermediate Representations for Better Transfer LearningCode1
DUET: 2D Structured and Approximately Equivariant RepresentationsCode1
Bilevel Continual LearningCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Reinforcement Learning for Mixed Autonomy IntersectionsCode1
Bridging Anaphora Resolution as Question AnsweringCode1
Relational Representation DistillationCode1
An Empirical Study on Cross-X Transfer for Legal Judgment PredictionCode1
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP ApplicationsCode1
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot LabelsCode1
HEAD: HEtero-Assists Distillation for Heterogeneous Object DetectorsCode1
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic EncryptionCode1
EEV: A Large-Scale Dataset for Studying Evoked Expressions from VideoCode1
Resolving Semantic Confusions for Improved Zero-Shot DetectionCode1
RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation NetworkCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Rethinking the Hyperparameters for Fine-tuningCode1
Rethinking Transfer Learning for Medical Image ClassificationCode1
Efficient Adaptation of Large Vision Transformer via Adapter Re-ComposingCode1
High-throughput molecular imaging via deep learning enabled Raman spectroscopyCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
Hyper-Representations for Pre-Training and Transfer LearningCode1
Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial SettingsCode1
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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