Hi there!
We implemented a real-world attack on one of the best public face detection system – MTCNN. We did it with different b/w stickers. It seems to be robust 😉

Paper: https://arxiv.org/abs/1910.06261

Code: https://github.com/edosedgar/mtcnnattack

Video demonstration: https://www.youtube.com/watch?v=OY70OIS8bxs

Comments are welcome!

I frequently see the distinction between one- and two-stage detectors, but I just cannot find a clear definition of what makes a one-stage detector and what makes a two-stage detector

Hey, we made a selection of the best AI articles published last month! Have a look at it!

As many people know, the relatively famous MVA master is taught in Paris, I’ think in Cachan (or is it Saclay?). It has a low tuition fee. I found another master by the Ecole Polytechnique which costs around 15k a year. Can anyone comment on it or the difference between them? Has anyone had direct experience?

Abstract

The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

Arxiv: https://arxiv.org/abs/1912.04958

PDF: https://arxiv.org/pdf/1912.04958.pdf

Youtube: https://www.youtube.com/watch?v=c-NJtV9Jvp0

Code: https://github.com/NVlabs/stylegan2

As a personal project, I’m trying to generate a comprehensive list of all companies working in the space. My idea is to then see and further group them and maybe see how the distribution is among companies who give cv as a service and companies who build on cv.

What do you guys think would be the best way to generate such a list? I’m targeting companies both big and small

This sub seems to have a rather alarmist reaction to patents which is a bit surprising to me. Coming from a Data and Software Engineering background, and holder of a couple patents myself with admittedly fairly broad claims for data systems, I have found that all existing use cases in the industry utilize patents as a sort of nuclear deterrence – to disincentive auxiliary lawsuits against you rather than as offensive claims.

So it’s a bit shocking to me how seriously this subreddit in particular takes patents as opposed to /r/computerscience or hacker news. Does the field of machine learning actively attack people who use similar techniques?

I’m a sophomore in CS at NC State University and I’ve got a technique working for taking precious research in a field and using it in a completely different field very effectively. This is my first paper so I’d love any tips or help from people who have done this before. Thanks!