www.youtube.com/watch?v=YQ58Sz23PT0

Join Tim Scarfe, Karol Zak, Tess Ferrandez and Hamid Reza Vaezi joze on a discussion of 3D CNNs for action detection (shots) in football games. We explain our baseline approach (2D CNN), interview Hamid and learn some background of 3DCNNS and then go through our new code (C3D pretrained on Sports1M dataset) in detail explaining the results. We didn’t yet try the I3D but expect it to be even better.

This should be of interest to anyone who is interested in learning the basics of action detection in videos.

I Read this paper a while ago and got super excited about it, but didn’t see it get implemented or talked about as i expected that it would, any ideas why?

Link to the paper: https://arxiv.org/abs/1903.03088

video: https://youtu.be/kwpb4TeZguo

repo: https://github.com/Suji04/Meme_with_ML

This is a fun project I made while learning about object detection with haar cascades. Like, everyone, I too like the thug life memes. But to make a meme face of your friend you have to add the pixelated glasses on your own. I wrote a python program that automatically finds the eyes and places the pixelated glasses on them. I first tried with a haar cascade that can find the eyes but that didn’t produce good results after placing the glasses. After that, I tried a face detecting haar cascade. My idea was…I don’t have to find the eyes I just have to know where the eyes lie on the face. I divided the face(a rectangle) into 4 parts by 3 horizontal lines. The eyes generally lie on the 2nd part from the top. So the program just places the glasses on the 2nd part of the face. To my surprise, this simple idea produced better results than the former one!

The author has released the pre-trained models a 50 epoch and a 100 epoch variant, alongside the dataset.

Tool: https://github.com/taki0112/UGATIT

I have generated some images using it: results

Tip: 256×256 images work faster, as well as around 50% of the image being the face helps.

I am not disappointed with the results, what do you think?

Hi all!

I am an MS student and was working on an Extreme Multilabel classification task in NLP. My solution required me to generate label embeddings which I did using my own novel method.

The task is yet to be completed but our University has an upcoming conference in which my prof wants me to submit this technique.

So my question is, how should I go about writing this paper? From what I know, once a novel embedding method is developed, it is tested on some downstream task like classification, etc. But my technique is for labels. So are there any such tasks for comparing and evaluating label embedding methods?

https://www.notjordanpeterson.com

About:

The technology used to generate audio on this site is a combination of two neural network models that were trained using audio data of Dr. Peterson speaking, along with the transcript of his speech. If you don’t know who Jordan Peterson is or what his voice sounds like, you can find links to his podcast, lectures, and YouTube videos on his website.

The first model, developed at Google, is called Tacotron 2. It takes as input the text that you type and produces what is known as an audio spectrogram, which represents the amplitudes of the frequencies in an audio signal at each moment in time. The model is trained on text/spectrogram pairs, where the spectrograms are extracted from the source audio data using a Fourier transform.

The second model, developed at NVIDIA, is called Waveglow. It acts as a vocoder, taking in the spectrogram output of Tacotron 2 and producing a full audio waveform, which is what gets encoded into an audio file you can then listen to. The model is trained on spectrogram/waveform pairs of short segments of speech.

The implementations used to create this site were forked from NVIDIA’s public implementations of Waveglow and Tacotron 2.

Disclaimer: This is not my product, I found it online.

Guys, I am planning to get into Research and come out with better models/functionalities etc to solve our day to day problems using machine learning.

What are the various problems that you think can be solved using machine learning and the areas where machine learning could be implemented but hasn’t yet due to real world constraints ?

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[sorry for my bad english]

I wonder why no one else has done it, so I do it myself. The results are impressive if you carefully do the cherry-picking.

It can babble car reviews in Clarkson’s style

> The Audi has more power, more torque, more grip, more braking power, and I agree with you, it’s a great car but look at it, look at that beautiful body.

> “Am I better off as a Bentley S197 when it’s half an inch wider?” which is what you had to ask yourselves is that’s really good car, when you buy it.

> The first of the two cars we looked at was the BMW M3, and from an engineering perspective, it doesn’t look like much to look at but that was because BMWs were made in a similar way to Fords but with more space and better-looking headlights, and that’s what really matters. The Porsche did as well.

Sometimes it can generate great introductions for The Stig

> Some say he once smeared his milk onto his body, and that he once had a threesome with a dead pig. All we know is, he’s called The Stig.

> Some say he has a penis implant. And that he is the most boring man in the world. All we know is, he’s called The Stig

Planning to improve the performance and make a showcase web app, any suggestions are appreciated.