MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications. Standard techniques for handling class-imbalance usually work by training on a re-weighted loss or on re-balanced data. Unfortunately, training overparameterized neural networks on such objectives causes rapid memorization of minority class data. To avoid this trap, we harness meta-learning, which uses both an ''outer-loop'' and an ''inner-loop'' loss, each of which may be balanced using different strategies. We evaluate our method, MetaBalance, on image classification, credit-card fraud detection, loan default prediction, and facial recognition tasks with severely imbalanced data, and we find that MetaBalance outperforms a wide array of popular re-sampling strategies.
Code (0)
등록된 구현이 없습니다.
Tasks
Fraud Detectionimage-classificationImage ClassificationMemorizationMeta-LearningVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
In many personalized recommendation scenarios, the generalization ability of a target task can be improved via learning with additional auxiliary tasks alongside this target task on a multi-task network. However, this me…
Predicting Louisiana Public High School Dropout through Imbalanced Learning Techniques
This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public well-being. Our goal is to predict students who are at risk of high school dropou…
Vocal Bursts Intensity PredictionImbalanced Ensemble Classifier for learning from imbalanced business school data set
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imba…
feature selectionGeneral ClassificationGenSample: A Genetic Algorithm for Oversampling in Imbalanced Datasets
Imbalanced datasets are ubiquitous. Classification performance on imbalanced datasets is generally poor for the minority class as the classifier cannot learn decision boundaries well. However, in sensitive applications l…
Fraud DetectionMedical DiagnosisExploring "Many in Few" and "Few in Many" Properties in Long-Tailed, Highly-Imbalanced IC Defect Classification
Despite significant advancements in deep classification techniques and in-lab automatic optical inspection models for long-tailed or highly imbalanced data, applying these approaches to real-world IC defect classificatio…
Knowledge DistillationMetric Learning