13 research outputs found
Lifelong Twin Generative Adversarial Networks
In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs). LT-GANs learns a sequence of tasks from several databases and its architecture consists of three components: two identical generators, namely the Teacher and Assistant, and one Discriminator. In order to allow for the LT-GANs to learn new concepts without forgetting, we introduce a new lifelong training approach, namely Lifelong Adversarial Knowledge Distillation (LAKD), which encourages the Teacher and Assistant to alternately teach each other, while learning a new database. This training approach favours transferring knowledge from a more knowledgeable player to another player which knows less information about a previously given task
Conditional GANs with Auxiliary Discriminative Classifier
Conditional generative models aim to learn the underlying joint distribution
of data and labels to achieve conditional data generation. Among them, the
auxiliary classifier generative adversarial network (AC-GAN) has been widely
used, but suffers from the problem of low intra-class diversity of the
generated samples. The fundamental reason pointed out in this paper is that the
classifier of AC-GAN is generator-agnostic, which therefore cannot provide
informative guidance for the generator to approach the joint distribution,
resulting in a minimization of the conditional entropy that decreases the
intra-class diversity. Motivated by this understanding, we propose a novel
conditional GAN with an auxiliary discriminative classifier (ADC-GAN) to
resolve the above problem. Specifically, the proposed auxiliary discriminative
classifier becomes generator-aware by recognizing the class-labels of the real
data and the generated data discriminatively. Our theoretical analysis reveals
that the generator can faithfully learn the joint distribution even without the
original discriminator, making the proposed ADC-GAN robust to the value of the
coefficient hyperparameter and the selection of the GAN loss, and stable during
training. Extensive experimental results on synthetic and real-world datasets
demonstrate the superiority of ADC-GAN in conditional generative modeling
compared to state-of-the-art classifier-based and projection-based conditional
GANs.Comment: ICML 202
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy