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 5576–5600 of 10307 papers

TitleStatusHype
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning SystemsCode0
Pre-trained Perceptual Features Improve Differentially Private Image GenerationCode0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
On statistic alignment for domain adaptation in structural health monitoringCode0
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source—0
Face2Text revisited: Improved data set and baseline results—0
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing—0
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
Cross-lingual Lifelong LearningCode0
Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking—0
Global Extreme Heat Forecasting Using Neural Weather Models—0
When does Parameter-Efficient Transfer Learning Work for Machine Translation?Code0
Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep LearningCode0
muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems—0
Action Recognition for American Sign Language—0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
Deep transfer learning for image classification: a survey—0
Causal Inference from Small High-dimensional Datasets—0
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer LearningCode0
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based TasksCode0
Human Gender Prediction Based on Deep Transfer Learning from Panoramic Radiograph Images—0
Evaluation of Transfer Learning for Polish with a Text-to-Text Model—0
Federated learning: Applications, challenges and future directions—0
Persian Natural Language Inference: A Meta-learning approachCode0
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine TranslationCode0
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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