Image segmentation refers to the process to divide an image into meaningful non-
overlapping regions according to human perception, which has become a classic topic since …

We study the problem of 3D object generation. We propose a novel framework, namely 3D
Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic …

We consider the two related problems of detecting if an example is misclassified or out-of-
distribution. We present a simple baseline that utilizes probabilities from softmax …

Given a grayscale photograph as input, this paper attacks the problem of hallucinating a
plausible color version of the photograph. This problem is clearly underconstrained, so …

In this work, we revisit the global average pooling layer proposed in [13], and shed light on
how it explicitly enables the convolutional neural network (CNN) to have remarkable …

Unlike human learning, machine learning often fails to handle changes between training
(source) and test (target) input distributions. Such domain shifts, common in practical …

Deep neural networks (DNNs) have demonstrated impressive performance in complex
machine learning tasks such as image classification or speech recognition. However, due to …

We develop a fully automatic image colorization system. Our approach leverages recent
advances in deep networks, exploiting both low-level and semantic representations. As …

The sound of crashing waves, the roar of fast-moving cars–sound conveys important
information about the objects in our surroundings. In this work, we show that ambient sounds …

This paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN
and Imagenet drove the advancement of scene understanding and object recognition. The …