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very clear. thanks. especially the cylcle picture at the end. clears up everything.

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chris griffin

All the world's technology started when Lucifer was cast out of Heaven by our heavenly Truly God

Are you aware of any AI that can intake a technical specification image (lets say it's a complex chart or graph, with hundreds of separate lines of text), and reproduce that tech specification image with variations that make it unique?

I'm writing a research paper on GAN usage and I would love to be able to cite you. You have compiled days worth of information into a 5 minute video. Simply phenomenal.

Great Job. I would like to ask few questions from you about using MONAI for unpair medical images. Since I have few data to work with and I am new to Machine learning . I intend to downsample the few images (about 500) I have and used the two scenario (Gen and Dis) for the two data sets to be trained. Can this approach work ? Which should I place at the Generative model and Discriminator?

Thanks a lot for this !

amazing content. It helped me a lot

For the initial conditions that work, have anybody look at how much wiggle room you have. Is there an epsilon-neighborhood of the initial state you can safely start from, and how small is epsilon?

Great job! Do you think we can make a lottery prediction by using the LTH method? Thanks.

Great video

Lol, its completely wrong what you say. Go study first before you make a video mate

The final bit causes problems: if __name__ == "__main__": model = WGAN(lr = 0.0001, silent = False) while(True): model.train() see below

ValueError: `tf.compat.v1.keras` Optimizer (<__main__.Adam_lr_mult object at 0x7fb49b609fa0>) is not supported when eager execution is enabled. Use a `tf.keras` Optimizer instead, or disable eager execution.

Hey can you help me understand how we can find anomaly on text document, and how we can specify something specifically as anomaly in an documnet.

hi how to make this video?

I absolutely loved your video, thank you so much for helping me understand cycle gan. Subscribed :).

So far so good!

Thanks

Really nice figures!

You nailed it!

nicely explained

1:27 "Generated...from absolutely nothing." Yeah, absolutely nothing...except for the millions of training images and roughly 0.7 megawatt hours of electricity used during training (aka "absolutely nothing"). Not to mention the 131.61 MWh of electricity (~$13,000) used to power multiple $150,000 GPUs during the development of StyleGAN2 (starting from StyleGAN).

Thanks for the presentation. May i get the code and dataset you used here?

I just want to ask why you don't use reshape directly to fuse batch & channel but change dimension formulation to (B,C,H,W) first ? And normalize kernel group by norm do not compute variance along the group because the mean is not zero

thanks!

This video is really informative, includes definition, intuition, and experiments done by the creator himself, while still being short. Thank you for the good work!

Man oh man! Geez!

Hi Matchue ,Nice Video ,Are we able to change color of the Pokemon

Does the paper really state that you can prune without training to convergence? I thought the big deal was that they showed the existence of a smaller network that can be trained if we use the same initialization. I don't think they were able to find this network without training to convergence.

Hello, in your TF2.0 implementation I'm not really sure how the Style Mapping is actually implemented? It doesn't look like it's actually used anywhere. Could you please explain? Thanks for the video and sharing. You explained the concepts very nicely.

Hi Matchue thank you for your presentation. Could you provide an example of a practical application using this method in the medical field?

Thanks for your nice presentation. Into the Anomaly-Agnostic Method slide, how the method can equally perform on any subdomain of anomalous samples?

Thanks for the presentation, what are some of the current real world application of anomaly detection on image data using generative adversarial networks

Nice presentation. Is there any image preprocessing done before giving it as an input to model, like converting to grayscale, resizing, applying filters, etc?

Nice Presentation. Are there any limitations of using the Anomaly-Agnostic method?

Thanks for the presentation Matthew! I was wondering with which processes does the discriminator in a GAN learn to classify between real and fake images.

Would you consider the anomaly-agnostic method or the classification method to be more effective?

In the BiGAN slide you mentioned that the method of training is different? Can you please explain it that what is the difference?

You mention that this method could also be used to detect medical anomalies/emergencies. However, finding out that you have an anomaly is only the first step, its just as important to find out where that anomaly is located. Do you think your high-level feature vector can also help locate such anomalies, or at the very least determine a rough center point of them in an image?

You mentioned that autoencoders have their limitations. Are these limitations related to distance measures? What other reconstruction loss functions we can use in GAN's? I guess you are using something similar to mse?

That's a nice presentation! How does it map on each pixel in the slide Image distribution mapping and Does it use any Image Processing technique for altering the pixel?

In the classification results, is there a justification of using those threshold values (0.2, 0.5, 0.8)? maybe due to some characteristics of the used data, or they were randomly chosen?

Hi Matthew, thank you for your presentation. The generator of the GAN architecture takes random numbers from a normal distribution and transforms it into an image, how are the numbers used to create these images if you could explain in a simple way, does it play around with the rgb values of pixels by assigning the numbers to them ?

Thank you for your presentation. Is there any example where they are using a similar system or these can be used?

Hello, thanks for the presentation. Regarding your final results, is there any process to determine the threshold? Will there be a threshold where the three evaluation matrices reach a balance?

We delivered. Please can you explain the results -False Positive and False Negative. In the Anomaly-Agnostic Method slide?

Nice presentation. 1- What is the input of the generating deep learning model of GAN? 2- Is it possible to use CNN for autoencoders to extract “high level features” for dimensionality reduction?