论文标题
通过联合发电和分类来减轻数据集失衡
Mitigating Dataset Imbalance via Joint Generation and Classification
论文作者
论文摘要
监督的深度学习方法在计算机视觉的许多实际应用中取得了巨大的成功，并有可能革新机器人技术。但是，对偏见和不平衡数据的明显绩效下降质疑这些方法的可靠性。在这项工作中，我们从数据集不平衡的角度解决了这些问题，这是由于对某些类别的注释培训数据的严重代表性及其对深层分类和生成方法的影响。我们通过将神经网络分类器与生成的对抗网络（GAN）相结合，从而弥补了代表性不足的班级的不足，从而介绍了一个联合数据集维修策略，通过产生其他培训示例。我们表明，综合培训有助于提高分类器和GAN的鲁棒性，以防止严重的阶级失衡。我们在三个截然不同的数据集上展示了我们提出的方法的有效性。该代码可从https://github.com/aadsah/imbalancecyclegan获得。
Supervised deep learning methods are enjoying enormous success in many practical applications of computer vision and have the potential to revolutionize robotics. However, the marked performance degradation to biases and imbalanced data questions the reliability of these methods. In this work we address these questions from the perspective of dataset imbalance resulting out of severe under-representation of annotated training data for certain classes and its effect on both deep classification and generation methods. We introduce a joint dataset repairment strategy by combining a neural network classifier with Generative Adversarial Networks (GAN) that makes up for the deficit of training examples from the under-representated class by producing additional training examples. We show that the combined training helps to improve the robustness of both the classifier and the GAN against severe class imbalance. We show the effectiveness of our proposed approach on three very different datasets with different degrees of imbalance in them. The code is available at https://github.com/AadSah/ImbalanceCycleGAN .
