tonic_section

5 ms·

1.
by tonic_section 6y ago
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 datapoints - you can only sample from some surrogate model of th
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by tonic_section 6y ago
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 message needed to transmit the compressed information and the d
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by tonic_section 6y ago
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 pretty closely, so there wouldn't be a point building a
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by tonic_section 6y ago
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 quantization, or just to ignore the quantization operation w
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by tonic_section 6y ago
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 examples where the compressed bitrate can be severa
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by tonic_section 6y ago
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.
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by tonic_section 6y ago
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 dataset contains relatively few pictures including text, 2)
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by tonic_section 6y ago
I think S3 permits up to 20k requests before they start billing IIRC.
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by tonic_section 6y ago
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 brightness change now. As for the random spots, that&#x
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by tonic_section 6y ago
I eventually shifted the models to S3, but thanks for the offer.
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by tonic_section 6y ago
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 publicly host model weights (~2 GB) please let me know!
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by tonic_section 6y ago
I pushed a workaround and provided extra instructions in the demo, so anyone experiencing errors should try that.
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by tonic_section 6y ago
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' UnpicklingError: invalid load key, '<'. ``
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by tonic_section 6y ago
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-dimensional representation of the original image - this is
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by tonic_section 6y ago
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 compressors need to learn a probability distribution over ea
16.
by tonic_section 6y ago
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. without trouble, but it's hard to legislate for errors in Ju
17.
by tonic_section 6y ago
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 generalizes to images of arbitrary size/resolution/content
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by tonic_section 6y ago
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 compressed representations at or below this bitrate. However,
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by tonic_section 6y ago
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 out directly and compress your own images in Google Co
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Show HN: Neural Image Compression Demo(colab.research.google.com)
78 points by tonic_section 6y ago|39 comments
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The Case of Jane Doe Ponytail(nytimes.com)
98 points by tonic_section 8y ago|17 comments
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by tonic_section 8y ago
CuPy shares a lot of the Numpy API. I've found it pretty interchangable in most applications.
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by tonic_section 8y ago
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 overconfident predictions, take a look at adversarial
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by tonic_section 8y ago
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.