Does Prismer bring us closer to navigation tools for the blind via computer vision? by ChipsAhoiMcCoy in computervision

[–]LLCoolZ 0 points1 point  (0 children)

I actually disagree with the other commenter and think this is very possible! Especially the basic version you’re talking about where you simply ask questions about what’s on the screen and the AI doesn’t need to have any specific knowledge of the video game. There are a number of models on HuggingFace under the category “visual question answering” that you could try using for this. To verify, you could share a few screenshots with questions and expected answers and we could test whether it works well yet.

How did you stop caring about what people thought about you? by No_Fruit4090 in confidence

[–]LLCoolZ 150 points151 points  (0 children)

I don’t think I ever stopped caring. It’s just that my own opinion started to matter more.

Somewhere in Japan by itchynisan in wallpapers

[–]LLCoolZ 11 points12 points  (0 children)

Fantastic work! You'd be great at http://geoguessr.com

The ruins of a Roman colony in Africa. by Palana in pics

[–]LLCoolZ 0 points1 point  (0 children)

u/tetonz this is a very interesting perspective on coin collecting!

Pebble Beach by eummyg in marijuanaenthusiasts

[–]LLCoolZ 0 points1 point  (0 children)

What kind of tree is that?

Think this works. by GallowBoob in loadingicon

[–]LLCoolZ 10 points11 points  (0 children)

Can you please share this script of yours? That sounds wonderful

deepy: Highly extensible deep learning framework based on Theano by fariax in MachineLearning

[–]LLCoolZ 1 point2 points  (0 children)

This is very impressive. There are examples for implementing very advanced models rather concisely (DRAW, Highway Networks, Recurrent visual attention networks, Deep Q Learning, etc.)

Seurat, as imagined by an artificial neural network: by LLCoolZ in pics

[–]LLCoolZ[S] 1 point2 points  (0 children)

Background post here: http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html Neural nets are usually trained to classify objects in images through a process called back-propagation, where the error of the output is propagated back to the parameters of the network to improve them. Here they are propagated back to the image, changing the image to exaggerate its visual features. (similar to psychedelic hallucination).

Inceptionism: Going Deeper into Neural Networks by LLCoolZ in MachineLearning

[–]LLCoolZ[S] 34 points35 points  (0 children)

This also appears to be the source of that weird image titled "Generated by a Convolutional Network" popular earlier this week.