Hello,

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I’m interested in a labeled dataset with the same 10 classes from CIFAR-10, but with the images extracted from imagenet (thus much higher quality and higher resolution) and with some reasonable class matching.

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Do you know if this dataset has been produced or is out there somewhere?

The most obvious ones are probably academic groups, but perhaps there are some non-profits I could identify?

Is it standard to save the noise distribution your model trained on to use and use the same values for transfer learning / fine tuning / inference?

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Or do people typically create a new distribution of noise for the model to learn at each new application?

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I’m assuming that during one entire training session you create the noise distribution once so that the model is learning the function of turning that noise into features of the images right?

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Thanks in advance!

I have been using sci-kit optimize to do hyperparameter search (using gp_minimize specifically) for a neural network. I am working on a binary classification problem with a significant class imbalance. I have been using a batch size of 10, but just came across a tweet and notebook by Francois Chollet where he recommended using a high batch size in class imbalance problems in order so that each batch contains at least a few positive examples.

My question is can I just take the networks with the best network architectures I found via my hyperparameter search where I used a batch size of 32, but just retrain them using the same hyperparameters but using a higher batch size?

Or, would batch size have a significant effect on hyperparameter optimization, and I would be better off just redoing hyperparameter optimization but this time with a larger batch size?

Going off of that, any recommendations for how to select batch size? My data contains between 400,000 – 500,000 samples, and I’m feeding in 7 features to the network.

On a similar not of dealing with class imbalance problems – my sample data is weighted to begin with (I am working with a physics problem and the weights for each sample is the probability that that sample will occur), but I was thinking about increasing the weights of the positive data points to maybe help minimize the effect of class imbalance. Thoughts on this?

I hope my question(s) makes sense, thanks for any help!

New blog post discussing flow-based generative models and the various coupling transforms that exist. We touch on the recent paper on Integer Discrete Flows toward the end.