A deep learning model for long-tail visual recognition

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.