Immortal333

>>> The cheapest automatic Tissot is around 700USD.

Is the engineering/logistics behind Mechanical watches really costlier than Oneplus phones ?

Chaining has its own benefits. But I think this doesn't fit the definition of "Pythonic". Again, "Pythonic" is highly debatable. But, You can always break down big chain of operations, into smaller chain using good variable naming in-between.

Many operations are implemented as iterator in python on list, like filter, groupby. Looking at your code, its looks like you're not doing lazy computation. (Correct me if I wrong). This could be huge performance impact, depending upon use case of list.

Yes, Exactly same as you described.

Yes, FAISS not FLANN. my mistake. cann't correct it now. because edit time is gone.

I've never worked at a FAANG, too. But main reason I want to work at one is that ability to do project at a scale which is not possible anywhere. Few project are no use for small companies/startups.

For example: Optimizing compile time (no need to invest for extra 1 minute speed up), working on high quality labelled data (i came from ML background, this is not possible in most of startups), analytics on data (questionable ethically), working on Ad platforms, working on large scale system.

In the last, Imagine, even making simple changes have bigger impact on real world.

It might be possible that these people have tried on more platforms (Face recognition APIs) but only reported those where they got good accuracy in terms of defeating system.

I personally would like to see tests done on facebook by uploading these images and checking if it can recognize it.

This is tested on existing models/Face Recognition API which means locked pre-trained models. So, They might have learned way to add pixels such that model outputs very different embedding. This is know issue in deep learning [0][1][2].

I believe, Model trained on cloaked images would defeat its purpose and make this technique useless.

[0] Su, Jiawei, Danilo Vasconcellos Vargas, and Kouichi Sakurai. "One pixel attack for fooling deep neural networks." IEEE Transactions on Evolutionary Computation 23.5 (2019): 828-841.

[1] Guo, Chuan, et al. "Countering adversarial images using input transformations." arXiv preprint arXiv:1711.00117 (2017).

[2] Liu, Yanpei, et al. "Delving into transferable adversarial examples and black-box attacks." arXiv preprint arXiv:1611.02770 (2016).

Hint for solving: Esoteric programming language