Attribute-wise sample generator: a module for generalized long tail data

Daao Yu; Pengfei Gu

doi:10.1117/12.3011819

7 December 2023 Attribute-wise sample generator: a module for generalized long tail data

Daao Yu, Pengfei Gu

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129411C (2023) https://doi.org/10.1117/12.3011819
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Existing Long-Tail classification (LT) methods ignore attribute class imbalance and focus only on addressing class imbalance where the head class has more samples than the tail class. In fact, even if the classes are balanced, the samples in each class can still be long-tailed due to attribute variability. The latter is fundamentally more general and difficult than the former, not only because attributes are implicitly included in most datasets, but also because they are more complex to construct. Therefore, we introduce a new research question, generalized long-tail classification (GLT), to jointly consider these two imbalances. And the ASG model is proposed to deal with the long-tail problem of attributes on the data set. ASG is a generalized class rebalancing method, which aims to balance the classes and attributes of the training data. It uses a feature displacement model to extract feature displacements, and then generates new samples according to the distribution of attributes in each class. Experiments prove that ASG has achieved good results on various datasets.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Daao Yu and Pengfei Gu "Attribute-wise sample generator: a module for generalized long tail data", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129411C (7 December 2023); https://doi.org/10.1117/12.3011819

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KEYWORDS

Education and training

Head

Data modeling

Statistical modeling

Performance modeling

Image classification

Sampling rates

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