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Found 9 papers, 5 papers with code
Equivariant Manifold Flows
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences.
Neural Manifold Ordinary Differential Equations
To better conform to data geometry, recent deep generative modelling techniques adapt Euclidean constructions to non-Euclidean spaces.
Differentiating through the Fréchet Mean
Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold.
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
We show that we can select a layer of the source model to perturb without any knowledge of the target models while achieving high transferability.
Fashion++: Minimal Edits for Outfit Improvement
We introduce Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability.
Adversarial Example Decomposition
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations.
Intermediate Level Adversarial Attack for Enhanced Transferability
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models.
Semantic Segmentation with Scarce Data
Semantic segmentation is a challenging vision problem that usually necessitates the collection of large amounts of finely annotated data, which is often quite expensive to obtain.
Generative Adversarial Perturbations
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.
