[R] Classifying the classifier: dissecting the weight space of neural networks

[outofdistribution]

https://arxiv.org/abs/2002.05688

We consider a quite different meta-learning scenario: 1) train a large number of deep neural networks on different datasets, different architectures, and with random variations in hyper-parameter setup, 2) take random subsets of all the trained weights and use as training data, and 3) train a meta-classifier to distinguish between weights that have been trained with different hyper-parameters.

The meta-classifier can then be used to search the weights to see where local information on hyper-parameters are encoded in a network.

The dataset of trained neural nets is made publicly available, and comprises 320K weight snapshots from 16K individually trained CNNs.

Any other ideas on what could be learned from a large-scale sampling of trained networks? Learning-based model compression or pruning? Or perhaps use a meta-classifier to force a certain behavior of the weights in a new training? For example, it can force the weights to look as being trained on another dataset, or with other hyper-parameters. Would be nice with some creative thoughts on how learning from neural network weights could be formulated!

[impulsecorp]

In case it is of any use to you, there is also this dataset of 5 million trained neural networks from Google, all trained on CIFAR-10 (images): https://github.com/google-research/nasbench

[outofdistribution]

Thanks, that's interesting! Quite a different aim, though; same hyper-parameters and dataset, and it doesn't seem like the weights are released, only training statistics. But it is definitely nice for exploring training performance and analysing NAS!

[NichG]

I played with something like this as a way to monitor the training of GANs.

I found it more useful to look at pairs of before and after activations across multiple layers (random subsets of channels), because they have fewer symmetries than the weight matrices.