**PointAugment** is a an 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.

AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of [ADAM](https://paperswithcode.com/method/adam). In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian: (i) achieves 1.80%/1.45% higher accuracy on ResNets20/32 on Cifar10, and 5.55% higher accuracy on ImageNet as compared to ADAM; (ii) outperforms ADAMW for transformers by 0.27/0.33 BLEU score on IWSLT14/WMT14 and 1.8/1.0 PPL on PTB/Wikitext-103; and (iii) achieves 0.032% better score than [AdaGrad](https://paperswithcode.com/method/adagrad) for DLRM on the Criteo Ad Kaggle dataset. Importantly, we show that the cost per iteration of AdaHessian is comparable to first-order methods, and that it exhibits robustness towards its hyperparameters.

AdaHessian

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

PointAugment

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

**GroIE** is an RoI extractor which intends to overcome the limitation of existing extractors which select only one (the best) layer from the [FPN](https://paperswithcode.com/method/fpn). The intuition is that all the layers of FPN retain useful
information. Therefore, the proposed layer introduces non-local building blocks and attention mechanisms to boost the performance.

Source	PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com