Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Semantic representations in higher sensory cortices form the basis for robust, yet flexible
behavior. These representations are acquired over the course of development in an …

Decomposing a scene into its shape, reflectance and illumination is a fundamental problem
in computer vision and graphics. Neural approaches such as NeRF have achieved …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

Abstract We introduce Interpolation Consistency Training (ICT), a simple and computation
efficient algorithm for training Deep Neural Networks in the semi-supervised learning …

Recent works on domain adaptation reveal the effectiveness of adversarial learning on
filling the discrepancy between source and target domains. However, two common …

Training classifiers under fairness constraints such as group fairness, regularizes the
disparities of predictions between the groups. Nevertheless, even though the constraints are …

Multi-domain image-to-image translation has gained increasing attention recently. Previous
methods take an image and some target attributes as inputs and generate an output image …

We present GraphMix, a regularization method for Graph Neural Network based semi-
supervised object classification, whereby we propose to train a fully-connected network …

Few-shot or one-shot learning of classifiers requires a significant inductive bias towards the
type of task to be learned. One way to acquire this is by meta-learning on tasks similar to the …