16 research outputs found
Art of singular vectors and universal adversarial perturbations
Vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has been
attracting a lot of attention in recent studies. It has been shown that for
many state of the art DNNs performing image classification there exist
universal adversarial perturbations --- image-agnostic perturbations mere
addition of which to natural images with high probability leads to their
misclassification. In this work we propose a new algorithm for constructing
such universal perturbations. Our approach is based on computing the so-called
-singular vectors of the Jacobian matrices of hidden layers of a
network. Resulting perturbations present interesting visual patterns, and by
using only 64 images we were able to construct universal perturbations with
more than 60 \% fooling rate on the dataset consisting of 50000 images. We also
investigate a correlation between the maximal singular value of the Jacobian
matrix and the fooling rate of the corresponding singular vector, and show that
the constructed perturbations generalize across networks.Comment: Submitted to CVPR 201
Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models
Recent advances in diffusion models enable many powerful instruments for
image editing. One of these instruments is text-driven image manipulations:
editing semantic attributes of an image according to the provided text
description. % Popular text-conditional diffusion models offer various
high-quality image manipulation methods for a broad range of text prompts.
Existing diffusion-based methods already achieve high-quality image
manipulations for a broad range of text prompts. However, in practice, these
methods require high computation costs even with a high-end GPU. This greatly
limits potential real-world applications of diffusion-based image editing,
especially when running on user devices.
In this paper, we address efficiency of the recent text-driven editing
methods based on unconditional diffusion models and develop a novel algorithm
that learns image manipulations 4.5-10 times faster and applies them 8 times
faster. We carefully evaluate the visual quality and expressiveness of our
approach on multiple datasets using human annotators. Our experiments
demonstrate that our algorithm achieves the quality of much more expensive
methods. Finally, we show that our approach can adapt the pretrained model to
the user-specified image and text description on the fly just for 4 seconds. In
this setting, we notice that more compact unconditional diffusion models can be
considered as a rational alternative to the popular text-conditional
counterparts
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
We introduce a simple autoencoder based on hyperbolic geometry for solving
standard collaborative filtering problem. In contrast to many modern deep
learning techniques, we build our solution using only a single hidden layer.
Remarkably, even with such a minimalistic approach, we not only outperform the
Euclidean counterpart but also achieve a competitive performance with respect
to the current state-of-the-art. We additionally explore the effects of space
curvature on the quality of hyperbolic models and propose an efficient
data-driven method for estimating its optimal value.Comment: Accepted at ACM RecSys 2020; 7 page