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 8476–8500 of 10307 papers

TitleStatusHype
Adversarial Transfer Learning for Punctuation Restoration—0
Style transfer-based image synthesis as an efficient regularization technique in deep 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
Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning—0
Adversarial Training Helps Transfer Learning via Better Representations—0
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis—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
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation—0
PAC Learning Guarantees Under Covariate Shift—0
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—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
Adversarial Network Compression—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
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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