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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7676–7700 of 10307 papers

TitleStatusHype
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Source-Free Unsupervised Domain Adaptation: A Survey—0
Modeling Social Norms Evolution for Personalized Sentiment Classification—0
The Comparison of Individual Cat Recognition Using Neural Networks—0
A Modular and Unified Framework for Detecting and Localizing Video Anomalies—0
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It—0
Model Inversion Robustness: Can Transfer Learning Help?—0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce—0
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy—0
Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition—0
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem—0
Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression—0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function—0
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment—0
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—0
Long-Tailed Learning Requires Feature Learning—0
Model Transport: Towards Scalable Transfer Learning on Manifolds—0
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction—0
A model is worth tens of thousands of examples—0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts—0
Modular Deep Learning—0
Show:102550
← PrevPage 308 of 413Next →
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