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 1426–1450 of 10307 papers

TitleStatusHype
Compressing BERT: Studying the Effects of Weight Pruning on Transfer LearningCode1
Distance-Based Regularisation of Deep Networks for Fine-TuningCode1
SentenceMIM: A Latent Variable Language ModelCode1
A deep learning framework for solution and discovery in solid mechanicsCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI DataCode1
Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical StudyCode1
Geometric Dataset Distances via Optimal TransportCode1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial RobustnessCode1
Multilingual acoustic word embedding models for processing zero-resource languagesCode1
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
ManyModalQA: Modality Disambiguation and QA over Diverse InputsCode1
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural NetworkCode1
Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured WebCode1
EEV: A Large-Scale Dataset for Studying Evoked Expressions from VideoCode1
Lipschitz Lifelong Reinforcement LearningCode1
PoPS: Policy Pruning and Shrinking for Deep Reinforcement LearningCode1
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and RecommendationCode1
LTP: A New Active Learning Strategy for CRF-Based Named Entity RecognitionCode1
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer LearningCode1
TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document ImagesCode1
Stance Detection Benchmark: How Robust Is Your Stance Detection?Code1
Source Model Selection for Deep Learning in the Time Series DomainCode1
Side-Tuning: A Baseline for Network Adaptation via Additive Side NetworksCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
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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