Human Pose estimation is an important problem that has enjoyed the attention of the Computer Vision community for the past few decades. It is an important step towards understanding people in images and videos. This post covers the basics of 3D Human Pose Estimation and reviews the literature on the topic.

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Article Link: https://blog.nanonets.com/human-pose-estimation-3d-guide/

Multiple groups agree on this issue:

“Assessing Generalization in Deep Reinforcement Learning” https://bair.berkeley.edu/blog/2019/03/18/rl-generalization/

We present a benchmark for studying generalization in deep reinforcement learning (RL). Systematic empirical evaluation shows that vanilla deep RL algorithms generalize better than specialized deep RL algorithms designed specifically for generalization. In other words, simply training on varied environments is so far the most effective strategy for generalization.

“Quantifying Generalization in Reinforcement Learning” https://openai.com/blog/quantifying-generalization-in-reinforcement-learning/

Generalizing between tasks remains difficult for state of the art deep reinforcement learning (RL) algorithms. Although trained agents can solve complex tasks, they struggle to transfer their experience to new environments. Even though people know that RL agents tend to overfit — that is, to latch onto the specifics of their environment rather than learn generalizable skills — RL agents are still benchmarked by evaluating on the environments they trained on. This would be like testing on your training set in supervised learning!

Why is this issue specific to deep RL? Is it just simply the evaluation metrics the field has been using (training on the test set)?

I originally figured it was just some landmark-based filtering/graphics tricks like most of the other filters, but looking at people’s selfies a bit closer, there seem to be a lot of GAN-like generative artifacts (especially in the hair) that I feel wouldn’t be there if it is just a graphics effect:

https://twitter.com/Midbeast/status/1126769681312862208

If it is a GAN, I have no idea how they would do it in real time, though. Thoughts?

Hi,

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Suppose we want to estimate the distribution of entities in one dimension x and how the distribution evolves over time. For the sake of example the dimension can represent the spatial position of the entities, e.g. in east-west direction. Unfortunately the entities do not have identifiers, so we can not tell if two measurements x_1 and x_2 belong to the same entity or not if they arrive at different times.

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The observability of the entities is limited which means that current x positions are not known at all times. Measurements of x arrive with varying temporal density. Thus, binning by time would lead to some time bins having few, others having a lot of data points.

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If we assume that the total number fof entities is constant, how could we estimate the distribution over x, given t, in such a way that the temporal density is accounted for? I’d imagine a system where measurements in times of low density have a longer time-to-live, i.e. contribute to the density estimation for longer times compared to times when a lot of measurements arrive.

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Is this a stochastic filtering problem? I’d be grateful for any hints what papers or textbook chapters to read to understand this issue better. My experience in the field is mostly limited to classification and regression with only limited exposure to density estimation and unsupervised learning. So, apologies if the question is trivial, I currently just don’t know where to start reading.

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Thanks!

Hey Redditors!

I maintained HyperLearn ( https://github.com/danielhanchen/hyperlearn Faster ML algos on Python ), but from Feb – May I was a bit busy with uni + work. But since stuff has calmed down, I was just asking everyone here on what fast and useful ML algos people would like to see, where maybe other packages lack in.

My main aims for HyperLearn are:

1. Delete all C/C++ dependencies, and rely solely on Numba + Scipy LAPACK.
2. Focus on specific algos that are extremely relevant.

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And on algos, I wanted to focus on:

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a. Randomized + Dense PCA on Sparse Matrices (without converting sparse to dense). decomposition.PCA [people tend to use pure SVD, but it “can” have different results than without removing mean”]

b. Porting Eigendecomposition via MRRR for the top K eigenvectors. [Scipy still hasn’t..] decomposition.EIGH

c. Fixing all memory copies for SVD, and support Randomized SVD on sparse matrices. decomposition.SVD

d. Fix up LinearSolve and place it into 1 module with LSMR (super fast sparse solve), combine with Cholesky, SVD, Eig, etc solving + Ridge. linear_model.solve

e. And finally, introduce a Python only modified version of Spotify’s ANNOY library (a limited nearest neighbor KD-Tree based on other heuristics I found to be useful).

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I’m just “guessing” the top 5 seem useful, as I myself have had many issues / struggles with other package algos. I’m aiming to make the final package easily installable with only Scipy + Numba as it’s prereqs (no more C/C++).

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If anyone else has opinions on what algos people want to see, but current packages lack in, please do! [Notice my field of knowledge is also limited….] If you want to help, PLEASEEE MSG me!!! I wantttt help!

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Finally, check out NVIDIA’s cuML https://github.com/rapidsai/cuml ! I’m part of their team making GPU algos super fast! For eg – UMAP runs in 2 minutes or so vs 15 minutes for Fashion MNIST.

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Thanks!!! 🙂

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**PS Interesting find – If you decorrelate your data / apply whitening / cholesky whitening, it can *sometimes improve your neural net training!

I have been working on data integration for the biological domain. Is there any research paper published on integration?

Can this be published in a journal or conference? Can you suggest some of them?