One of my favorite papers is Generalized End-to-End Loss for Speaker Verification. It describes a way to learn a model that can derive embeddings that are highly representative of the characteristics of the voice from speech segments. It does so with only the identity of the speakers as labels. It’s also an approach that can be applied to any kind of data beyond just voice, provided that the data is grouped by source (e.g. for speech it is grouped by speaker, for faces it is grouped by identity, …).

A classical approach that will work without any labels is using an autoencoder. Not being up to speed in that domain, are there autoencoder-based frameworks that have proved to extract powerful features, more so that the classical auto-encoder pipeline?

Do you also know of approaches beyond these that achieve this goal?

https://arxiv.org/abs/1907.09019

TL;DR we consider images that show illusion in Human, represent the images in ImageNet-trained VGG-19, and find that representational distances around an illusion image are “weird” (non-monotonic). This is mostly unlike distances around control images, at least to the extend that we checked.

What do you think? We are now considering extensions, and would very much appreciate ideas, even negative.

DeepMind’s self-supervised (a.k.a. unsupervised) network, CPC, surpassed AlexNet’s performance on ImageNet. If I understand correctly, both CPC and AlexNet used the same set of training images. CPC just didn’t use labels, while AlexNet did. So, what about instances where a self-supervised network can be trained on 10,000x as much data as would be economically feasible to label? In these cases, are supervised learning’s days numbered? Or not so fast?

The application I’m personally most interested in is self-driving cars. By putting cameras on consumer cars, you are limited really only by your fleet size, your data centre costs, and your customers’ monthly bandwidth limits for their home wifi in terms of how much data you can collect. Tesla, for instance, has over 500,000 cars with 360-degree cameras, GPUs or ASICs to run neural networks, and the ability to connect to wifi and upload training data. Elon Musk recently mentioned Tesla’s plans to “do unsupervised massive training of vast amounts of video”. Tesla’s Director of AI, Andrej Karpathy, also recently tweeted his strong support for self-supervised learning. So this question is more than hypothetical.

I know that decision stumps are used as basic classifiers in boosting, but what other basic classifiers can be used? Can we use any classifier? SVMs are pretty accurate, and boosting, well, boosts the algorithm. So why not use SVMs as the basic binary classifier and boost the hell out of it with AdaBoost? What’s stopping us?

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PS: If this is a beginner question, tell me to delete it.

Hey, I am a Masters student and a TA for Machine Learning at my uni. What i noticed is that while teaching my students i would learn concepts much quicker. Hence why started a new twitter channel @Daily_ML_Tips so i can learn and teach more people directly. Twitter Link Here

Feel free to give me a follow and tweet at me if u have any specific ML questions. 🙂

have a great day

Back there with another question, have anyone considered disentangling/learning on style of temporal structure in videos ?

I have seen many examples of ‘artistic’/photorealistic style transfer usage on single images/frames, and also using a temporal constraint to transfer it coherently.. But i ain’t askin’ about spatial style transfer enforced by temporal structure, but rather exploiting the temporality of different videos (films would be very useful i can imagine) and transferring ’em..

I have two datasets that I ran a label encoder and onehotencoder on. A is the actual and B is the prediction.

I would like to be able to come up with the accuracy between the two. For example in B, the last row is different so I would like calculate an accuracy of 75%.

Is there a nice pythonian way of doing this?

A

B

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This is my preference for work enviroment right now. I find that they are hard to look up, since early stage companies aren’t focused on PR. So maybe there’s some indirect methods of looking up these companies.

Few days ago ,I see a article introduceed ShuffleNet V2 .

Based on my curiosity I trained ShuffleNet V2 on MNIST datase, and I got 99.14% accuracy .
( code here : https://github.com/allen108108/Model_on_MNIST/blob/master/MNIST%20-%20ShuffleNetV2.ipynb ).

The result looks good , but I got higher accuracy ( almost 99.5% ) when I trained a typically CNN model on the same dataset
( code here : https://github.com/allen108108/Model_on_MNIST/blob/master/MNIST%20-%20CNN.ipynb).

Is that normal situation ? Did I make some mistakes ?

PS : the ShuffleNet V2 model that I used : https://github.com/opconty/keras-shufflenetV2