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 2926–2950 of 10307 papers

TitleStatusHype
Multi-Domain Evolutionary Optimization of Network Structures—0
Generalization error of min-norm interpolators in transfer learning—0
Multi-modal Transfer Learning between Biological Foundation Models—0
Depth F_1: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic GeneralizabilityCode0
Robust Few-shot Transfer Learning for Knowledge Base Question Answering with Unanswerable Questions—0
Learning to Transfer for Evolutionary Multitasking—0
Data-Centric AI in the Age of Large Language Models—0
Information Guided Regularization for Fine-tuning Language ModelsCode0
Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data—0
Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects—0
CNN Based Flank Predictor for Quadruped Animal Species—0
Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques—0
Probabilistic Deep Learning and Transfer Learning for Robust Cryptocurrency Price PredictionCode0
Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization—0
Neuro-symbolic Training for Reasoning over Spatial LanguageCode0
Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly—0
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction—0
Skin Cancer Images Classification using Transfer Learning Techniques—0
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images—0
Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
The Wisdom of a Crowd of Brains: A Universal Brain Encoder—0
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain—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