Hi everyone! I recently implemented BiGAN in Keras, with some modifications (hinge loss, gradient penalty, etc.). Then, with that, I made a script to find similarities in r/EarthPorn images using BiGAN’s encoder, as well as clustering images using k-means clustering in the feature space. I’ve uploaded my code to github aswell.

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Here is an image, on the left are target images, and on the right are the 7 most “similar” images in the dataset, in order.

https://i.imgur.com/owXSkXf.png

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Here is another, where each row is a cluster, detecting by k-means in the encoder’s feature space.

https://i.imgur.com/wgiKuiK.png

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Lastly, here is another image. On the top are images generated by BiGAN. On the bottom are real images of landscapes (1st and 3rd row) and their reconstruction using BiGAN’s E and G (2nd and 4th row).

https://i.imgur.com/lcUzGZk.png

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Github implementation:

https://github.com/manicman1999/Keras-BiGAN

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Enjoy!

Wired posted this article about using AI to assess toxicity on social media.

We are starting to see the impact of using AI/ML on everyday interactions and communications, not to mention policing. One of the most interesting and relevant conclusions:

The use of Perspective and other similar technologies could thus be mistakenly used to police and censor legitimate LGBTQ speech on online platforms. If AI tools focus on misleading signals—such as the use of specific words, rather than a message’s intent—such models will make little progress in removing hate speech.

Some games (like early 3D platformers) tend to have drastically different style from another, often mainstream games, in terms of its cartoony or perhaps photorealistic look. The superresolution networks that NVIDIA used doesn’t use disentanglement i believe, which possibly restricts its generalization and usage on ultra-LQ textures..

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Might someone look into joining games with similiar graphics styles from early era and todays’s era (even though the style similiarity is questionable, sometimes games within the same franchises look different, so perhaps a style disentangler trained on regular datasets might be used to diffrentiate between ’em) and learn a network to recognize a style and generate high-resolution details based on dat ?

I’m looking for any non-image applications of GANs or in general adversarial learning. are these being used in industry at all?

Hi, guys!

There are so many data visualization techniques.

Among them, Barnes-Hut t-SNE is one of the most popular tool and is using many research papers.

So I have summarized how to use this tool, and anyone can follow it very easily.

If you have interest, visit below link and enjoy it 🙂

https://github.com/kdhht2334/Barnes-Hut_t-SNE

Hi Reddit community, I’m currently self-learning/exploring reinforcement learning. I have downloaded a few codes to try out and to get a feel of the code. There is a piece of code [code A] about using A3C for CartPole-v0, and it manages to learn very well. And another piece of code [code B] that uses DQN for LunarLander-v2, it managed to train a smart agent too.

Then I change the environment in code A (uses A3C) to LunarLander-v2 and MountainCar-v0, there weren’t any errors, but the agent fails to learn. Likewise, I change the environment in code B (uses DQN) to CartPole-v0 and MountainCar-v0, it didn’t learn as well.

Why is it so? Is it because different environments have different rewards system? Or the hyperparameters that worked for CartPole-v0 does not work for LunarLander-v2?

Granted on most modern datasets like CIFAR10/100/ImageNet we are cheating no matter what because we use the test set as a validation set. However it’s important to compare apple to apple. I’ve always reported last test accuracy, but I’m seeing more and more papers report the best one, which gives them a non-negligible boost.

Best: In practice if we don’t have a test set we would typically deploy the model that has the best validation accuracy. So here we report “test accuracy” but we mean “validation accuracy of the model we would deploy”.

Last: Makes it harder to overfit the validation/test set, and is arguably closer to the real generalization accuracy you would get.

Is there a consensus on best practice?

Hi, So I have been working on and developed a model for ASL fimger spelling (not the words, just apphabets). I have an accuracy of 80-90% and can implement it real time. The major advantage is the image processing algorithm I use which allows me to localise the hand anywhere in the frame and so I don’t need a ROI box. Also the second advantage is the higher accuracy. The model combines blocks from YOLO and inception networks

So I was wondering if this work is suitable for being published as I have no experience about it. I see many videos on Reddit and online hence I’m not sure. Also if it is, is there any upcoming conference where I can submit to

Thanks

All the normalizing flows-based papers I read (NICE, RealNVP, Glow, etc.) show experiments on high dimensional image datasets. I am looking for works that analyze the capacity of NFs to learn simple 1/2d distributions. I am aware of the 2d experiments in [Rezende and Mohamed, 2015] but, as far as I understand, for the 2d datasets they train by directly minimizing KL (and do not train using samples) because the analytic inverse of Planar flow does not exist.