This paper I saw today on “inferring crowd density from a moving drone camera especially when perspective effects are strong” will appear in IROS, and it seems like it cannot possibly be used for good or even other related problems. I have also seen similar papers for mass surveillance with drones in the proceedings of a few top-tier CV conferences, and I’m sure there are many similar cases that you all have seen.

Should these conferences be held accountable for publishing papers with inevitably harmful applications?

I recently generated some new watch designs using StyleGAN, and I thought some of you may find it interesting. All 50,000 images I used to train were sourced from the /r/watches subreddit. These are the results I was able to achieve after 48 hours of training on a GTX 980 Ti. Considering the hardware Nvidia recommends, I’m pretty happy with it!

https://evigio.com/post/generating-new-watch-designs-with-stylegan

I have a small and highly imbalanced low-resolution image dataset with 6 classes where 50% of the observations are from a single class. With unconditional GANs, I can get stably obtain samples with seemingly sufficient diversity under most setups (architecture, losses, etc).

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In the conditional case, there is significant class leakage in the samples generated. I’ve tried various standards on class conditioning such as, (1) conditional batch norm in the generator; (2) a projective layer in the critic and; (3) increasing the batch size significantly to cover more modes in each batch. (1) and (2) seem to be helping, and (3) doesn’t appear to be helping with sample diversity and is making sample quality worse. Training is still in early stages though, so maybe things change (or modes collapse).

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Are there any strategies or heuristics for conditional GANs specifically dealing with the class imbalanced case? At this point, I’m considering using balanced subsampled batches in each iteration or weighing the hinge loss by class distribution and hoping for the best.

arxiv: https://arxiv.org/abs/1903.05157

Abstract:

Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception. While deep learning for perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images, end-to-end demonstrations of successful attacks, which manipulate the physical environment and result in physical consequences, are scarce. Moreover, attacks typically involve carefully constructed adversarial examples at the level of pixels. We demonstrate the first end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are surprisingly easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective, and others that are less vulnerable (e.g., driving straight). Further, we use network deconvolution to demonstrate that the attacks succeed by inducing activation patterns similar to entirely different scenarios used in training.

Recently published my first, first-authored paper in the ML domain. Thought this would be a good place to share and talk about it.

Essentially, we answer the question: Can we paint a line on the road in a way that would confuse an autonomous vehicle?

• so far we looked only at vision (camera) based end-to-end Imitation Learning and Reinforcement Learning models
• wanted to bring up discussion about adversarial ML against self-driving vehicles

So, I know that the code for GPT-2 345M is publicly released, and there are people who’ve been training GPT-2 on various things, such as magic cards, cat names, and facebook messenger posts. I guess my question is, what is preventing people from training their own 1.5B model? Heck, what does the parameters mean?

I’ve got a coding background but I’m only familiar with the basics of neural nets; only been following it because media synthesis is really fun.

Hey all, we are excited to announce that we just released a framework that has the basic tools for building a collaborative assistant (that uses in-game chat) in Minecraft: https://github.com/facebookresearch/craftassist .

To encourage the wider AI research community to use the CraftAssist platform for their own experiments, we are open-sourcing the framework, as well as a baseline assistant and the tools and data we used to build it. The purpose of building such an assistant is to facilitate the study of agents that can complete tasks specified by dialogue, and eventually, to learn from dialogue interactions. The release includes sequential step-by-step data of human players building more than 2500 houses in Minecraft, semantic segmentation data for those houses, and a large-scale natural language semantic parsing data set.

You can read more about our research in the blogpost: https://ai.facebook.com/blog/craftassist-platform-for-collaborative-minecraft-bots/

We’d love to hear from you!