Hi, during summer I worked on a project with the objective of removing people or objects from photos. Person-remover uses a pretrained YOLO to detect them and then feeds the resulting bounding boxes to the generator of a pix2pix which I trained from zero on Paris dataset. Even though the generator wasn’t trained with the purpose of filling person-shaped objects, the results are pretty great and seems to generalize well to unseen photos or video.

Any ideas on how to improve the results even further?

Repo: https://github.com/javirk/Person_remover

Is it domain specific? Does Ruders’ blog/paper still hold up: https://ruder.io/multi-task/?

Hi all,

I did a toy project for Korean NER tagger(in progress). If you are interested in Korean Named Entity Recognition, try it. (This NER tagger is implemented in PyTorch)

If you want to apply it to other languages, you don’t have to change the model architecture, you just change vocab, pretrained BERT(from huggingface), and training dataset.

https://github.com/eagle705/pytorch-bert-crf-ner

I often times work with people (medical studies) with a huge “knowledge” on statistical methods but none of the required basics or understanding what goes on inside some algorithms. That’s perfectly fine because after all that’s not their job but mine.

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But over time, I’ve come across a few problems where (due to not finding the “needed significance”) some really basic over-sampling was applied. I’ve thrown together a really simple example, that anyone should be able to follow (without any deep statistical knowledge) to showcase what could happen – maybe it helps you or you can use it to your help:

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https://stroemer.cc/resample-imbalanced-data/

Something that seems to be under-discussed in machine learning is why we bother with momentum method in the first place.

Suppose we are training a classifier and the loss function has two local minima, one of which is global. Suppose by sheer unluck, the gradient descent gets stuck in the worse local minima. If you ask around as to what can be done, you will hear answers like “oh just use the momentum method, it gets you out of the local minima”.

First, there is no guarantee you will be out of the local minima (only if the difference between the current and previous iterate is large enough do you have a chance), and more importantly,

Second, great, you have found the global mimina and….you have just potentially overfitted your classifier.

In other words, we are looking for local minima (or even just some point associated with the loss function) with good generalization properties, and I don’t think momentum methods guarantees that.

Has there been any research on the generalization properties of the minima that you find and what algorithm get you the best minima, not in terms of how small the loss is, but how well it achieves generalization?

Hello everyone,

I’d like to share some work I have done in recasting logic gates into a differentiable form, thereby enabling gradient descent through a classical program.

https://youtu.be/hSEinrmMm9A

Codebase will be released soon.

I’m looking for resources/papers where someone has tried to create an AI that can play a simulation racing game like IRacing, Assetto Corsa or Project Cars 2. I know that there are nets that can play basic racing games like mario kart but I was wondering if there are ones that can play in a complex physics environment where you need to employ real strategies to win at a competitive level.