SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset. SOSELETO is based on the following simple intuition: some source examples are more informative than others for the target problem. To capture this intuition, source samples are each given weights; these weights are solved for jointly with the source and target classification problems via a bilevel optimization scheme. The target therefore gets to choose the source samples which are most informative for its own classification task. Furthermore, the bilevel nature of the optimization acts as a kind of regularization on the target, mitigating overfitting. SOSELETO may be applied to both classic transfer learning, as well as the problem of training on datasets with noisy labels; we show state of the art results on both of these problems.
Code (1)
Tasks
Bilevel OptimizationClassificationGeneral ClassificationTransfer LearningSimilar Papers 제목 키워드 기반
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling
Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified d…
Denoisingimage-classificationImage ClassificationPrivate Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, such as medical records, could pose serio…
Transfer LearningToward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two sam…
Data AugmentationDiversityMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRobust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels
Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detectio…
Bilevel OptimizationPushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternativ…
Multi-Label Learning