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 5251–5275 of 10307 papers

TitleStatusHype
Representation learning from videos in-the-wild: An object-centric approach—0
Representation Purification for End-to-End Speech Translation—0
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery—0
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning—0
Representation Topology Divergence: A Method for Comparing Neural Network Representations.—0
Representation Transfer by Optimal Transport—0
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression—0
Re-presenting a Story by Emotional Factors using Sentimental Analysis Method—0
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model—0
Reprogramming Language Models for Molecular Representation Learning—0
Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation—0
Repurposing Decoder-Transformer Language Models for Abstractive Summarization—0
Research Frontiers in Transfer Learning -- a systematic and bibliometric review—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—0
Research on Task Discovery for Transfer Learning in Deep Neural Networks—0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought—0
Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking—0
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance—0
Resource-efficient domain adaptive pre-training for medical images—0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Resources and Experiments on Sentiment Classification for Georgian—0
Response by the Montreal AI Ethics Institute to the European Commission's Whitepaper on AI—0
Restricted Orthogonal Gradient Projection for Continual Learning—0
RE-Tagger: A light-weight Real-Estate Image Classifier—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