We present a principled approach to uncover the structure of visual data by solving a novel
deep learning task coined visual permutation learning. The goal of this task is to find the …

We present a principled approach to uncover the structure of visual data by solving a deep
learning task coined visual permutation learning. The goal of this task is to find the …

Inner product-based convolution has been a central component of convolutional neural
networks (CNNs) and the key to learning visual representations. Inspired by the observation …

Deep feature spaces have the capacity to encode complex transformations of their input
data. However, understanding the relative feature-space relationship between two …

In this paper, we explore methods of complicating selfsupervised tasks for representation
learning. That is, we do severe damage to data and encourage a network to recover them …

A simple approach to learning invariances in image clas-sification consists in augmenting
the training set with transformed versions of the original images. However, given a large set …

We introduce a novel method for representation learning that uses an artificial supervision
signal based on counting visual primitives. This supervision signal is obtained from an …

Learning representations of images that are invariant to sensitive or unwanted attributes is
important for many tasks including bias removal and cross domain retrieval. Here, our …

In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …

Abstract “Thinking in pictures,”[1] ie, spatial-temporal reasoning, effortless and
instantaneous for humans, is believed to be a significant ability to perform logical induction …