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 6451–6475 of 10307 papers

TitleStatusHype
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
Anomaly Detection in Images—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
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—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
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—0
RE-Tagger: A light-weight Real-Estate Image Classifier—0
Rethinking Continual Learning for Autonomous Agents and Robots—0
Benchmarks and models for entity-oriented polarity detection—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
Rethinking Efficient Tuning Methods from a Unified Perspective—0
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning—0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Rethinking Importance Weighting for Transfer Learning—0
Adaptable image quality assessment using meta-reinforcement learning of task amenability—0
Rethinking Membership Inference Attacks Against Transfer Learning—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