Deep Bayesian Active Learning with Image Data

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).Alan Turing Institute Grant EP/N510129/1 EPSRC Grant EP/N014162/1 Qualcom

How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods

Bayesian Data Augmentation and Generative Active Learning for Robust Imbalanced Deep Learning

Deep learning has become a leading machine learning approach in many domains such as image classification, face recognition, and autonomous driving cars. However, its success is predicated on the availability of immense labelled training sets. Furthermore, it is usually the case that these data sets need to be well-balanced, otherwise the performance of the trained model is compromised. The outstanding performance of deep learning compared to other traditional machine learning approaches is therefore traded off by the need of a significant amount of human resources for labelling and computational resources for training. Designing effective deep learning approaches that can perform well using small and imbalanced labelled training sets is essential since that will increase the use of deep learning in many real-life applications. In this thesis, we investigate several learning approaches that aim to improve the data efficiency in training deep models. In particular, we propose novel effective learning methods that enable deep learning models to perform well with relatively small and imbalanced labelled training sets. We first introduce a novel theoretically sound Bayesian data augmentation (BDA) method motivated by the fact that the current dominant data augmentation (DA), based on small geometric and appearance transformations of the original training samples, does not guarantee the usefulness and the realism of the generated samples. We formulate BDA with the generalised Monte-Carlo expectation maximisation (GMCEM).We theoretically show the weak convergence of GMCEM and introduce an implementation of BDA based on a variant of the generative adversarial network (GAN). We empirically demonstrate that our proposed BDA performs better than the dominant DA above. One of the drawbacks of BDA mentioned above is that the generation of synthetic training samples is performed without considering their informativeness to the training process. Therefore, we next propose a new Bayesian generative active deep learning (BGADL) approach that aims to train a generative model to produce novel informative training samples. We formulate this algorithm based on a theoretically sound combination of the Bayesian active learning by disagreement (BALD) and BDA, where BALD guides BDA to produce synthetic samples. We provide a formal proof that these generated samples are informative for the training process. We provide empirical evidence that our proposed BGADL outperforms BDA and BALD with respect to training efficiency and classification accuracy. The Bayesian generative active deep learning above does not properly handle class imbalanced training that may occur in the updated training sets formed at each iteration of the algorithm. We extend BGADL with an approach that is robust to imbalanced training data by combining it with a sample re-weighting learning approach. We empirically demonstrate that the extended BGADL performs well on several imbalanced data sets and produce better classification results compared to other baselines. In summary, the contributions of this thesis are the introduction of the following novel methods: Bayesian data augmentation, Bayesian generative active deep learning, and a robust Bayesian generative active deep learning for imbalanced learning. All of those contributions are supported by theoretical justifications, empirical evidence and published or submitted papers.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201