https://openreview.net/forum?id=Bke_DertPB

https://arxiv.org/abs/1907.05681

source code: https://github.com/dterjek/adversarial_lipschitz_regularization

TL;DR: alternative to gradient penalty

(FKA Virtual Adversarial Lipschitz Regularization)

Hi, I’m trying to adjust bids(Max. CPC) of products, advertised on google ads, based on their past performance like Clicks, Impressions, Conversions, Product Price, etc. Google provides those data in CSV format. Now I want to utilize those data for bidding intelligently instead of using Smart Bidding feature provided by Google itself. Is there any good Machine Learning algorithm that can be used for such a case. Or, no need to use any ML algorithm here? Or, any better idea that can be applied for this use-case? Thanks in advance.

Hi all, I am currently trying to implement AAGAN paper

I found some inconsistency in the paper.

In Generator we have 5 encoding blocks. As far as I understand they change number of channels in such way: 32 – 64 64- 128 128- 256 256 – 512 512 – 1024

But the residual layers that follow the enBlocks have c512. What should I do with it? I make res layers with c1024 but I don’t know is it correct. Because it seems my network does not work correctly (it stucks at some point and G loss decreases VEERY slowly, while the D converges pretty fast. The dehazed images look better than hazed, but they are noticeably darker than the original ones and they do not remove haze effect completely.

I will share my code today later after fixing some code issues.

Hi, i’m considering writing a paper on upcoming ICML 2020, with my theoretical works and Primary experiments(e.g., MNIST things). At the moment my results is quite closed/or outperform some other SOTA models, but i think there’s no time to finish other two or three experiments for main conference paper. If i’d accepted with some workshop tracks in a conference, is it allowed to republish the same paper with supplement experiments/and some other theoretical claiming? If it’s ok, then are there any possible penalty for future submission? (e.g., lack of novelty compared to my prev workshop paper, …) Thank you in advance and happy new year!

Hello,

I am following Lex’s podcast recently, however, I have noticed some researchers like Anima says that he is a clown and mouthpiece of Elon musk. Also, he had blocked many peoples on Twitter, which is maybe questionable? I would like to ask the community here how do they see the podcast is I am still newbie in the field?

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Thanks,

So perhaps this is a misguided question but to my (limited) understanding the biggest hurdle to applying deep computer vision models from research(ex: classification or object detection) to a new problem is data collection. If you have access to a video stream the problem is: what are the best images to annotate. To me this sounds like it would fall under the Active Learning umbrella, however, I’ve seen a very limited set of papers[1,2,3,4] applied to this. The experiments on these papers also aren’t that great because they don’t reflect reality (images in a video are not i.i.d.)

Am I missing something? Perhaps a better way of selecting what images to annotate that’s not related to active learning?

[1] Deep Active Learning for Object Detection

[2] Cost-Effective Active Learning for Deep Image Classification

[3] The power of ensembles for active learning in image classification

[4] Reducing Class Imbalance during Active Learning for Named Entity Annotation

I fine-tuned a model to generate Netflix movie/tv show plots with one click. Maybe writers can use it as movie prompts for ideation to start writing their next script.

Preview

Project: https://github.com/ismaileldesouky/netflix-movie-generator

Like I want to make something long-term that can be its own unique app in the future but I want to have some small projects to show for it before that happens