An Exploration of Three Approaches to Automating Data Augmentation for Machine Learning

PhD Thesis Proposal Defence


Title: "An Exploration of Three Approaches to Automating Data Augmentation 
for Machine Learning"

by

Mr. Tsz Him CHEUNG


Abstract:

Data Augmentation is an effective way to improve the generalization of deep 
learning models. However, the current augmentation methods primarily rely on 
manual operations, such as flipping and cropping for image data. These methods 
are often designed based on human expertise or trial and error. Meanwhile, 
Automated Data Augmentation (AutoDA) is a promising research direction that 
treats the data augmentation process as a learning task and discovers the most 
effective ways to augment the data. In this thesis, we examine recent AutoDA 
research and introduce four new methods to enhance AutoDA using 
composition-based, mixing-based, and generation-based approaches.

Existing composition-based AutoDA methods apply a fixed policy to the entire 
dataset and mainly focus on image classification tasks. In response, we propose 
AdaAug to learn class- and instance-adaptive augmentation policies and MODALS 
to learn modality-agnostic latent space augmentation strategies. For 
mixing-based AutoDA, we introduce TransformMix to learn transformation and 
mixing augmentation strategies from data. Compared to previous mixing-based 
methods, TransformMix takes into account the saliency information of the input 
images and produces more compelling mixed images that contain accurate and 
important information for the target tasks. Regarding generation-based AutoDA, 
we present AutoGenDA to address imbalanced classification tasks. Specifically, 
AutoGenDA identifies and transfers label-invariant changes across data classes 
through image captions and text-guided generative models. We also propose an 
automated search strategy to adapt the AutoGenDA augmentation to each data 
class, leading to better generalization.


Date:                   Tuesday, 27 August 2024

Time:                   4:00pm - 6:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Dit-Yan Yeung (Supervisor)
                        Prof. Nevin Zhang (Chairperson)
                        Prof. Albert Chung
                        Dr. Qifeng Chen