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 9451–9475 of 10307 papers

TitleStatusHype
Self-supervised Knowledge Distillation Using Singular Value DecompositionCode0
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models—0
Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging—0
Multi-task dialog act and sentiment recognition on MastodonCode0
Neural Networks Regularization Through Representation LearningCode0
State-of-the-art and gaps for deep learning on limited training data in remote sensing—0
AI Reasoning Systems: PAC and Applied Methods—0
Multi-Task Learning with Incomplete Data for Healthcare—0
Towards more Reliable Transfer Learning—0
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks—0
Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection—0
Neonatal Pain Expression Recognition Using Transfer Learning—0
End-to-End Learning on 3D Protein Structure for Interface PredictionCode0
Balanced Distribution Adaptation for Transfer Learning—0
Mammography Dual View Mass Correspondence—0
Coherence Modeling Improves Implicit Discourse Relation Recognition—0
A Multi-lingual Multi-task Architecture for Low-resource Sequence LabelingCode0
Deep Residual Network based Automatic Image Grading for Diabetic Macular EdemaCode0
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification—0
Detecting Offensive Tweets in Hindi-English Code-Switched Language—0
Structured Variationally Auto-encoded Optimization—0
Breast cancer histology classification using Deep Residual NetworksCode0
A Named Entity Recognition Shootout for German—0
A Helping Hand: Transfer Learning for Deep Sentiment Analysis—0
A Food Photography App with Image Recognition for Thai Food—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