Online Hyper-parameter Learning for Auto-Augmentation Strategy

2019-05-17 · ICCV 2019 10 · Chen Lin, Minghao Guo, Chuming Li, Yuan Xin, Wei Wu, Dahua Lin, Wanli Ouyang, Junjie Yan

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.

📄 PDF Abstract BibTeX arXiv:1905.07373

Code (1)

Awesome-AutoAug-Algorithms/AWS-OHL-AutoAug pytorch

Tasks

Data Augmentation

Hypernetwork-Based Augmentation

2020-06-11 · Chih-Yang Chen, Che-Han Chang

Data augmentation is an effective technique to improve the generalization of deep neural networks. Recently, AutoAugment proposed a well-designed search space and a search algorithm that automatically finds augmentation …

Data Augmentation

Learning Data Augmentation with Online Bilevel Optimization for Image Classification

2020-06-25 · Saypraseuth Mounsaveng, Issam Laradji, Ismail Ben Ayed, David Vazquez 외

Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data augmentation hyperparameters requires domain knowledge or a computationally demanding searc…

Bilevel Optimization Classification Data Augmentation General Classification+2

Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL

2021-06-30 · NeurIPS 2021 12 · Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen Roberts

Despite a series of recent successes in reinforcement learning (RL), many RL algorithms remain sensitive to hyperparameters. As such, there has recently been interest in the field of AutoRL, which seeks to automate desig…

Data Augmentation Hyperparameter Optimization Reinforcement Learning (RL)

Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction

2018-11-01 · Hongyuan Zhan, Gabriel Gomes, Xiaoye S. Li, Kamesh Madduri 외

Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust model parameters automatically based on the…

BIG-bench Machine Learning Computational Efficiency Hyperparameter Optimization Prediction+5

The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition

2021-05-27 · Junxiao Shen, John Dudley, Per Ola Kristensson

Deep learning approaches deliver state-of-the-art performance in recognition of spatiotemporal human motion data. However, one of the main challenges in these recognition tasks is limited available training data. Insuffi…

Action Recognition Data Augmentation Generative Adversarial Network GPU+2