A deep learning model for long-tail visual recognition

Zhengwu Yuan; Yunxing Cheng; Chan Tang; Ze Chen

doi:10.1117/12.2628702

22 April 2022 A deep learning model for long-tail visual recognition

Zhengwu Yuan, Yunxing Cheng, Chan Tang, Ze Chen

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 1217415 (2022) https://doi.org/10.1117/12.2628702
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Deep learning has a wide range of applications and far-reaching influence in our production and life, but the training of models requires a lot of data. In the real world, data acquisition requires a lot of costs, and the distribution of data is often uneven, with a small number of categories occupying a large number of samples, making the overall data present a long-tailed distribution. This makes convolutional neural network often perform poorly when training data are heavily class-imbalanced. In this work, enhance the feature extraction capabilities of the base model by add attention mechanism, and use the regularization technology mix-up algorithm to enhance the long-tail data. compared several state-of-the-arts techniques on the benchmark datasets imbalanced CIFAR10 and CIFAR100, that our method provides consistent and significant improvements over previous models.

Citation Download Citation

Zhengwu Yuan, Yunxing Cheng, Chan Tang, and Ze Chen "A deep learning model for long-tail visual recognition", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 1217415 (22 April 2022); https://doi.org/10.1117/12.2628702

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