Optimizing the Training Set to Improve Model Specificity on Target Domains
Creators: name: Yao, Yue
Publisher: name: The Australian National University
Year: 2023

description: Over the last few decades, researchers have worked to improve deep learning models, striving to achieve human-level performance. To reach such a level, the development of deep learning has relied on the joint confluence of the model (i.e., algorithms) and data. Noticeably, the optimization of data has received relatively little attention in comparison to the optimization of the model. This thesis aims to strengthen deep learning models' specificity from a data-centric perspective. We highlight that the training data, i.e., the data which deep learning models learned from, has significant potential for improvement. Specifically, we analyzed the training set quality, and designed algorithms to perform training set optimization, under various scenarios. We first aim to understand the training set quality, which is critical to the success of the resulting models, and forms the foundation of my thesis. In this thesis, we point out that data diversity and domain gap are two pivotal aspects describing training data quality. The former encodes the extent to which training samples differ, and the latter characterizes the distribution difference between training and testing. We argue that the two factors are intrinsically correlated and should be jointly considered. We formulate training data quality as a function of both data diversity and domain gap, and quantitatively analyze it on 872 different training sets created by geometric image transformations and neural rendering. We verify that a joint objective of high diversity and low domain gap characterize high-quality training data, and apply this idea in composing better training sets. We then focus on an application called the training set search, in which we aim to construct a competent training set under a certain scenario. We first consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training set from a large-scale data pool such that a competitive model can be obtained. We propose a search and pruning (SnP) solution to this training data search problem, tailored to object re-identification (re-ID), an application aiming to match the same object captured by different cameras. The SnP solution provides us with training sets 80% smaller than the source pool while achieving a similar or even higher re-ID accuracy. We further consider a training set search scenario where we have access to the target domain, and instead would like to use synthetic data to construct an alternative training set. To address the content-level misalignment between synthetic and real, we propose an attribute descent approach that automatically optimizes engine attributes to enable synthetic data to approximate real-world data. Extensive experiments on image classification and object re-ID confirm that adapted synthetic data with attribute descent can be effectively used in three scenarios: training with synthetic data only, training data augmentation and numerically understanding dataset content. Apart from studying training set quality on training a high accuracy model. We also consider building the privacy-preserved training set which leads to the privacy-preserved model. We studied the feature fusion problem in data, and proposed a new strategy to filter out unwanted features from data based on our feature extractor, tailored to the brain-computer interface, which its input signal electroencephalography (EEG) is known as feature fused and rich in all kinds of information from our brain. Our experimental results on an alcoholism dataset show that our novel model can filter out over 90% of alcoholism information on average from EEG signals, with an average of only 4.2% useful feature accuracy lost, showing effectiveness for our proposed task.