We present PointAugment, a new auto-augmentation framework that automatically
optimizes and augments point cloud samples to enrich the data diversity when we
train a classification network. Different from existing auto-augmentation
methods for 2D images, PointAugment is sample-aware and takes an adversarial
learning strategy to jointly optimize an augmentor network and a classifier
network, such that the augmentor can learn to produce augmented samples that
best fit the classifier. Moreover, we formulate a learnable point augmentation
function with a shape-wise transformation and a point-wise displacement, and
carefully design loss functions to adopt the augmented samples based on the
learning progress of the classifier. Extensive experiments also confirm
PointAugment's effectiveness and robustness to improve the performance of
various networks on shape classification and retrieval.

这篇论文提出了 PointAugment 自动增强框架，使用对抗性学习策略自动优化和增强点云样本以增加数据多样性，在形状分类方面取得良好表现。

PointAugment：一种针对点云分类的自动增强框架

PointAugment: an Auto-Augmentation Framework for Point Cloud  Classification

Data augmentation is critical to the success of modern deep learning
techniques. In this paper, we propose Online Hyper-parameter Learning for
Auto-Augmentation (OHL-Auto-Aug), an economical solution that learns the
augmentation policy distribution along with network training. Unlike previous
methods on auto-augmentation that search augmentation strategies in an offline
manner, our method formulates the augmentation policy as a parameterized
probability distribution, thus allowing its parameters to be optimized jointly
with network parameters. Our proposed OHL-Auto-Aug eliminates the need of
re-training and dramatically reduces the cost of the overall search process,
while establishes significantly accuracy improvements over baseline models. On
both CIFAR-10 and ImageNet, our method achieves remarkable on search accuracy,
60x faster on CIFAR-10 and 24x faster on ImageNet, while maintaining
competitive accuracies.

本文介绍了使用 Online Hyper-parameter Learning for Auto-Augmentation 解决基于离线搜索增广策略所存在的高昂成本问题。在 CIFAR-10 和 ImageNet 数据集上，我们的方法取得了显著的准确率提高，并且速度分别比现有方法快了 60 倍和 24 倍，同时保持了有竞争力的准确性。