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 8176–8200 of 10307 papers

TitleStatusHype
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources—0
Transfer Learning with Point Transformers—0
Transfer Learning with Pre-trained Conditional Generative Models—0
Transfer Learning with Random Coefficient Ridge Regression—0
Transfer Learning with Sparse Associative Memories—0
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)—0
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection—0
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text—0
Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents—0
Transfer Neyman-Pearson Algorithm for Outlier Detection—0
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data—0
Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants—0
Transfer of knowledge among instruments in automatic music transcription—0
Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share—0
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Model—0
Transfer of Temporal Logic Formulas in Reinforcement Learning—0
Transfer Operator Learning with Fusion Frame—0
Transfer Prototype-based Fuzzy Clustering—0
Transferrable End-to-End Learning for Protein Interface Prediction—0
Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks—0
Transferred Fusion Learning using Skipped Networks—0
Transferred Q-learning—0
Transfer Regression via Pairwise Similarity Regularization—0
Transfer Reinforcement Learning in Heterogeneous Action Spaces using Subgoal Mapping—0
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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