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 4801–4825 of 10307 papers

TitleStatusHype
Out-of-Task Training for Dialog State Tracking Models—0
Overcome Anterograde Forgetting with Cycled Memory Networks—0
Overcoming data scarcity with transfer learning—0
Overcoming Label Ambiguity with Multi-label Iterated Learning—0
Frozen Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks—0
Overhead MNIST: A Benchmark Satellite Dataset—0
Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling—0
π_0.5: a Vision-Language-Action Model with Open-World Generalization—0
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy—0
PAC-Bayes Analysis of Sentence Representation—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
PAC Learning Guarantees Under Covariate Shift—0
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning—0
Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking—0
PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics—0
PAD: Towards Efficient Data Generation for Transfer Learning Using Phrase Alignment—0
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation—0
Pairwise Discernment of AffectNet Expressions with ArcFace—0
PalmProbNet: A Probabilistic Approach to Understanding Palm Distributions in Ecuadorian Tropical Forest via Transfer Learning—0
Palm Vein Recognition via Multi-task Loss Function and Attention Layer—0
Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification—0
Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge—0
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation—0
PanoSwin: a Pano-style Swin Transformer for Panorama Understanding—0
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar—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