continuousml

9 ms·

1.
Out-of-Distribution Machine Learning(github.com)
13 points by continuousml 2y ago|2 comments
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by continuousml 2y ago
Out-of-distribution (OOD) problems present critical challenges in modern machine learning systems. As AI models are increasingly deployed in real-world applications, they often encounter data that differs significantly from what they were
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by continuousml 3y ago
Thank you all so much for the overwhelming support. The repo has gained almost 100 stars today, and that brings a lot of visibility to the fantastic works in the OOD community. I hope this sparks even more interest in the area, so eventuall
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by continuousml 3y ago
You're welcome! Thanks for the wisdom.
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by continuousml 3y ago
Done. Thank you so much ;)
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by continuousml 3y ago
There is a well-known survey in the field that characterizes anomaly/novelty + ood detection as 'generalized ood detection'. Check it out: https://arxiv.org/pdf/2110.11334.pdf
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by continuousml 3y ago
By the way, I just happened to click on your profile...Please correct me for any misconception I may have lol.
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by continuousml 3y ago
Facts. I often think of it as some sort of outlier, intuitively. However, it is generally helpful to be familiar with the terms that many authors use when you navigate the literature ;)
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by continuousml 3y ago
Gotcha. Thank you so much for your feedback. This is actually a work in progress and I am drafting a nice logo + intro for it ;)
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by continuousml 3y ago
Thanks a lot!
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by continuousml 3y ago
Hi there. Thanks a lot for your interest. You are very close indeed, but there is a very minor technical difference. Consider a distribution of dogs. If you have a dog that is significantly different from the majority of the dogs in the dis
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by continuousml 3y ago
You are very welcome :)
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by continuousml 3y ago
Gotcha, thanks so much for your interest. I just did!
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by continuousml 3y ago
Thanks everyone for your interest. Since some people have wondered what out-of-distribution is, I'm going to talk about it briefly. It is an emerging trend in deep learning research aiming to address one of the current deficiencies tha
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by continuousml 3y ago
Thank you so much for your interest. Deep learning has had tremendous success recently but it often makes one important assumption, that is whatever data the model sees during deployment should be 'similar' to what it was trained
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by continuousml 3y ago
You're absolutely right. And I am in academia so I'm not aware of what's being done in industry. I've heard people prefer conformal prediction since it is simple and distribution-free. Also, I recommend this fantastic ar
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Out-of-Distribution(github.com)
45 points by continuousml 3y ago|28 comments
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by continuousml 3y ago
This repo aims to provide the most comprehensive, up-to-date, high-quality resource for OOD detection, robustness, and generalization in Deep Learning. Check it out and give a star to support me if you find it helpful ;) Thank you so much!
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Out-of-Distribution(github.com)
2 points by continuousml 3y ago|1 comments
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by continuousml 3y ago
This repo aims to provide the most comprehensive, up-to-date, high-quality resource for OOD detection, robustness, and generalization in Deep Learning.
21.
OOD Detection, Robustness, and Generalization(github.com)
1 points by continuousml 3y ago|1 comments
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by continuousml 3y ago
The most comprehensive, up-to-date, high-quality resource for OOD detection, robustness, and generalization.