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 5251–5275 of 10307 papers

TitleStatusHype
Learning to Transfer—0
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands—0
Learning to Transfer for Evolutionary Multitasking—0
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation—0
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning—0
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection—0
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies—0
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training—0
Learning Transferability in Deep Segmentation of Liver Metastases—0
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation—0
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models—0
Learning Transferable Feature Representations Using Neural Networks—0
Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling—0
Learning Transfers over Several Programming Languages—0
Learning ULMFiT and Self-Distillation with Calibration for Medical Dialogue System—0
Learning unbiased features—0
Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation—0
Learning Universal Policies via Text-Guided Video Generation—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
Learning Visually Consistent Label Embeddings for Zero-Shot Learning—0
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images—0
Learn it or Leave it: Module Composition and Pruning for Continual Learning—0
Learn to Talk via Proactive Knowledge Transfer—0
LEEP: A New Measure to Evaluate Transferability of Learned Representations—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