Learning useful representations with little or no supervision is a key challenge in artificial
intelligence. We provide an in-depth review of recent advances in representation learning …

Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

In this work, we perform unsupervised learning of representations by maximizing mutual
information between an input and the output of a deep neural network encoder. Importantly …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Recent advances in deep learning have shown exciting promise in filling large holes in
natural images with semantically plausible and context aware details, impacting …

Visible-infrared person re-identification (VI-ReID) is an important task in night-time
surveillance applications, since visible cameras are difficult to capture valid appearance …

Deep learning-based semantic segmentation methods have an intrinsic limitation that
training a model requires a large amount of data with pixel-level annotations. To address …

The generative adversarial network (GAN) framework has emerged as a powerful tool for
various image and video synthesis tasks, allowing the synthesis of visual content in an …