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 7751–7775 of 10307 papers

TitleStatusHype
Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning—0
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning—0
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition—0
MSIT\_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Sparse coding for multitask and transfer learning—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
A Meta-Learning Approach for Custom Model Training—0
Sparse Contrastive Learning of Sentence Embeddings—0
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning—0
MTG: A Benchmarking Suite for Multilingual Text Generation—0
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model—0
A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer—0
MuCaLe-Net: Multi Categorical-Level Networks to Generate More Discriminating Features—0
MUCS@Text-LT-EDI@ACL 2022: Detecting Sign of Depression from Social Media Text using Supervised Learning Approach—0
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation—0
Amazon Alexa AI’s System for IWSLT 2022 Offline Speech Translation Shared Task—0
A Checkpoint on Multilingual Misogyny Identification—0
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning—0
A Many Objective Optimization Approach for Transfer Learning in EEG Classification—0
Multi-Action Dialog Policy Learning with Interactive Human Teaching—0
MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction—0
Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications—0
Multi-Agent Collaboration for Multilingual Code Instruction Tuning—0
Multi-Agent Policy Transfer via Task Relationship Modeling—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