I just finished writing the blog post outlining the idea and process behind AI Against Humanity. Maybe it is of interest for some of you. Let me know what you think! 🙂
https://cpury.github.io/ai-against-humanity/

Previous posts:

1. https://www.reddit.com/r/MachineLearning/comments/bvwvoo/p_ai_against_humanity_play_cards_against_humanity/
2. https://www.reddit.com/r/MachineLearning/comments/c0n5a1/p_update_ai_against_humanity_now_with_an_ai/

Hi guys,

as a part of my master thesis, in which I try to predict how aesthetic an image is, I wrote a telegram bot that can rate images you send to it.

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Actually there are two bots, you can reach the first one at https://t.me/ImageAestheticsBot which will rate all kind of images. The second one is trained only with photos with people on it, so you can use that for photos like portraits. This one you can reach at https://t.me/ImageAestheticsPeopleBot .

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If you like to read something on how image aesthetics can be rated by neuronal nets NIMA: Neural Image Assessment is a good start.

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I would really like it if you try this bot and maybe give me some feedback if you think it’s useful.

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If you have questions feel free to ask and if you know some other subreddits where this post might fit in please tell me. This is my first post here so any advice about posting, if I did something wrong, would be cool too.

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Thanks in advance!

Hi everyone, I’ve been a member here for quite a while. After discussion with some members here I started m own ML blog on Medium.

After gaining some traction, a couple of people representing curated AI journals/websites some on Medium and some outside of Medium have been contacting me about housing my blog on their platform. Since many of the members here have active blogs and more publishing experience than me, I wanted to ask whats the right choice? Should I keep on going solo? Contribute to one or all of the platforms? And what does it mean to have my blog get published on someone else’s platform, in terms of content rights, benefits, etc?

Hi, Here’s an example of what I have in mind:

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I have 1000 face images. I use a face recognition model to convert each face to a 128D embedding.

I have a hunch that each embedding has encoded special characteristics of a face such as eye color, jaw type, male or female genders and lots of other attributes.

Now I want to somehow be able to classify each face (each embedding) based on the attribute that I choose. For example I want to find all faces that have green eyes and are male ONLY based on the embeddings.

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One way to do this is to classify the faces based on my attributes (gender, eye color, jaw type, …) and then train the embeddings on these attributes. But this approach takes a lot of time since I have to either make a dataset of faces containing different attributes or download it from somewhere and train a model on it.

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I was wondering if there is an unsupervised or semi-supervised approach to cluster the embeddings based on the attributes I choose (gender, eye color, jaw type, …) by only selecting a few of the faces that have these attributes and the model/method automatically tries to cluster the faces based on my chosen attributes.

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Simply finding the nearest neighbor of an embedding isn’t enough. For example I may choose a face that has green eyes (a rare factor) and find the nearest faces of that face’s embedding, but there is no guarantee that the nearest faces all have green eyes since they may have other stronger similarities that the embedding may have encoded (for example the same beards or brows).

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One way to account for this is to average a couple of embeddings of faces which have green eyes and then try to find the nearest faces. But then again, there is no guarantee that the embeddings are even ‘average friendly’ meaning that averaging them would results in the attribute that is shared between them to become stronger.

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So is this even possible and what is the fastest and highest quality approach to do it? Thanks.

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EDIT: Since I may not have explained my question in full detail, if there are any questions about this post, I’d be glad to explain furthur.

This is a list of habits I adopted since I started learning Deep Learning. They have helped me save time and be more effective.

http://blog.varunajayasiri.com/practices_learned_while_learning_machine_learning.html

Hello r/machinelearning,

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I’ve always been intrigued to visually map out the main parts of the NumPy API to serve as a gentle intro. Here it is:

https://jalammar.github.io/visual-numpy/

Interest article investigating the effect of using adversarially robust classifiers for neural style transfer, with a few examples. I felt the second set of examples are better than the first one at the beginning of the article, as it has more examples and also can compare ResNet vs VGG.

While style transfer using VGG seems to look less of like an image filter (at least when using a ResNet), it seems that using a adversarially robust ResNet helps and brings it closer to VGG at least qualitatively.

link to article

link to colab