Adversarial training with these examples enable the model to withstand a wide range of attacks by observing a variety of input alterations during training.
A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision.
Ranked #3 on 3D Point Cloud Linear Classification on ModelNet40
We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i. e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i. e., a scalar function over the 3D volume, with its levels denoting surfaces.
We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation.
We capture these subtle changes by applying an image translation network to refine the mesh rendering, providing an end-to-end model to generate new animations of a character with high visual quality.
In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models.
Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos.
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network.
Ranked #11 on Conditional Image Generation on CIFAR-10