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You mixed up implicit and explicit models. For anyone interested in the difference - implicit models such as GANs don't allow you to evaluate the probability density over datapoint…

The objective function used in these lossy neural compression schemes usually takes the form of a rate-distortion Lagrangian - the rate term captures the expected length of the mes…

Usually the lossless encoding is offloaded to a standard entropy coder, e.g. arithmetic, ANS, etc. because these approach the theoretical minimum rate given by the source entropy p…

There are a couple of solutions which work empirically - as you mentioned, one solution is a dithering-like differentiable relaxation where uniform noise is added, which simulates …

In terms of the decoded image, yes - it's very unlikely you would get something substantially different from the original image. But in terms of the bitrate it's not hard to find e…

Unfortunately you wouldn't have any guarantees on the output of any particular image though, just some reassurances about the expected behaviour over the training set.

Yeah, high frequency detail such as facial features for faraway figures or text tend to get washed out after compression - this is probably due to a couple reasons: 1) The training…

I think S3 permits up to 20k requests before they start billing IIRC.

Hey, thanks for bringing the brightness issue to my attention - turns out I wasn't normalizing the output correctly - I just pushed a fix and the output images don't have the brigh…

I eventually shifted the models to S3, but thanks for the offer.

Sorry, looks like both GDrive and Zenodo have exceeded the temporary download quotas, so the model checkpoints aren't available currently... If anyone has any solutions on how to p…

I pushed a workaround and provided extra instructions in the demo, so anyone experiencing errors should try that.

GDrive doesn't download the model checkpoints correctly sometimes, leading to the following error: ``` # Setup model I get an error in the function call 'prepare_model' UnpicklingE…

Yes, the model is not lossless as this would require learning the PDF in the original input space. However, the model does learn a conditional probability distribution over a lower…

As u/londons_explore mentioned, in theory you can train a model for lossless reconstruction - there are several papers about this, e.g. [1] is a good recent example. Lossless compr…

What was the error? I tried to make the demo notebook as robust as possible - you should be able to execute all cells in sequence once then execute cells out of sequence etc. witho…

The model was trained on a fairly image (~1e6) dataset of diverse high-resolution natural images (the Openimages dataset) - so there was no particular training domain, and generali…

During training, you can set a target bitrate by heavily penalizing examples which exceed the target rate in the rate-distortion objective - so the model should learn to produce co…

Hi everyone, I've been working on an implementation of a model for learnable image compression together with general support for neural image compression in PyTorch. You can try it…

CuPy shares a lot of the Numpy API. I've found it pretty interchangable in most applications.

The problem is that neural networks trained using maximum LL do not return calibrated probabilities, using e.g. the softmax output as 'confidence' of a model tends to result in ove…

How do you quantify the confidence of your model? Do you use a Bayesian model or just the log-likelihood? Because the latter can act strangely in some cases.