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 8226–8250 of 10307 papers

TitleStatusHype
Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning—0
Transformations between deep neural networks—0
Transformer-based approaches to Sentiment Detection—0
Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets—0
Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach—0
Transformer-Based Microbubble Localization—0
Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin—0
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students—0
Transformer Models for Drug Adverse Effects Detection from Tweets—0
Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers—0
Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection—0
Transforming Sequence Tagging Into A Seq2Seq Task—0
Transforming Social Science Research with Transfer Learning: Social Science Survey Data Integration with AI—0
Transforming Static Images Using Generative Models for Video Salient Object Detection—0
TransformMix: Learning Transformation and Mixing Strategies from Data—0
Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation—0
TransiT: Transient Transformer for Non-line-of-sight Videography—0
TransLandSeg: A Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba—0
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning—0
TransMIA: Membership Inference Attacks Using Transfer Shadow Training—0
TransNet: A Transfer Learning-Based Network for Human Action Recognition—0
TransNet: Transferable Neural Networks for Partial Differential Equations—0
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback—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