A week ago, I posted here that I have built a face detector to blur faces for videos with GitHub repository.

The entire project was based on OpenCV examples. So it’s obvious that the result can’t be good enough to use and slow.

So, I’ve been looking for better projects for face detection and one of the comments from my earlier post said using DeepPrivacy might be good. Sounds great, so I tried to run DeepPrivacy and I couldn’t run because of lack of GPU memory.

Okay…🤔 It looks like DeepPrivacy uses DSFD to detect faces. Shouldn’t I just use DSFD to detect faces and blur them?

There, I wrote the simple implementation of anonymizing videos by DSFD. https://github.com/JeiKeiLim/Anonymizing_video_by_DSFD

And I found lightDSFD which is a lightweight version of DSFD. So I also applied lightDSFD to anonymize videos. https://github.com/JeiKeiLim/Anonymizing_video_by_lightDSFD

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However, lightDSFD looks less accurate(obviously). It works most of the time but it shows failure when a person is laying down or the light is too dark, etc.

So far, DSFD looks great that I feel like I can actually publish videos that I can’t upload until the faces are anonymized. The problem is that it takes 11 hours to complete for 15 minutes of 60fps video.

…. I am still waiting for the first video to be finished. And if my calculation is correct, all videos will be processed in 51 days.

What are some machine learning topics that are applicable to cancer, or more broadly, biology, besides diagnostics?

I’m planning to switch to JAX for DL in CV. But however still having doubts about its future. Anyone with insights?

At the end of every year, I like to take a look back at the different trends or papers that inspired me the most. As a researcher in the field, I find it can be quite productive to take a deeper look at where I think the research community has made surprising progress or to identify areas where, perhaps unexpectedly, we did not advance. I’ve put together a post in which I give my perspective on the state of the field from this past year. The post is no doubt a biased sample of what I think is progress, but I hope it stimulates discussion about which subfields evolved or what priorities unexpectedly shifted in 2019.

Link to post: http://www.cachestocaches.com/2019/12/my-state-of-the-field/

A short summary/outline of my post for discussion:

• From AlphaZero to MuZero MuZero picks up where AlphaZero left off a couple years ago and makes significant advances using a learned model to enable rollouts without planning in pixel space.
• Representation Learning I’m particularly excited to see how recent progress in representation learning (like advances in “entity abstraction”) will help to blur the lines between black-box deep learning and old-school symbolic AI & classical planning.
• Supervised Computer Vision Research Cools (slightly) Research in this space has slowed, but related techniques, like network pruning and network compilation, have taken off this past year.
• Maturing Technologies
  • Graph Neural Networks
  • Explainable & Interpretable AI
  • Simulation Tools & Sim-to-Real
• Bittersweet Lessons No discussion of 2019 would be complete without a conversation about Rich Sutton’s “The Bitter Lesson” post and rebuttals.

What do you think were the most interesting advancements or shifts this year?