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 9326–9350 of 10307 papers

TitleStatusHype
Learning Robust, Transferable Sentence Representations for Text Classification—0
Reuse and Adaptation for Entity Resolution through Transfer Learning—0
Using Multi-task and Transfer Learning to Solve Working Memory Tasks—0
Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning—0
Zero-Resource Multilingual Model Transfer: Learning What to Share—0
Weakly-Supervised Localization and Classification of Proximal Femur Fractures—0
Transferrable End-to-End Learning for Protein Interface Prediction—0
Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards—0
Measuring Density and Similarity of Task Relevant Information in Neural Representations—0
An analytic theory of generalization dynamics and transfer learning in deep linear networks—0
Learning Physics Priors for Deep Reinforcement Learing—0
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning—0
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-MakingCode0
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis—0
Non-native children speech recognition through transfer learning—0
DT-LET: Deep Transfer Learning by Exploring where to Transfer—0
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations—0
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages—0
Domain Adaptation for Robot Predictive Maintenance Systems—0
On Reinforcement Learning for Full-length Game of StarCraft—0
A Meta-Learning Approach for Custom Model Training—0
Target Transfer Q-Learning and Its Convergence Analysis—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—0
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs—0
Transfer and Multi-Task Learning for Noun-Noun Compound InterpretationCode0
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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