Pruning Latent Neurons in Autoencoders using Early-Bird Tickets
R.F. Klazinga (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Marco Loog – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
J.C. van Gemert – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
CCS Liem – Graduation committee member (TU Delft - Multimedia Computing)
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Abstract
Autoencoders seek to encode their input into a bottleneck of latent neurons, and then decode it to reconstruct the input. However, if the input data has an intrinsic dimension (ID) smaller than the number of latent neurons in the bottleneck, this encoding becomes redundant.
In this paper, we study using the Early-Bird (EB) technique, a structural pruning method, to regularise and prune the redundant latent neurons. We do this for both linear-layer and convolutional autoencoders, on 1D and 2D data. We find that increasing the strength of EB regularisation specifically on the latent layer can lead to all redundant latent neurons (and no more) being removed in one training run.
We also compare using EB in this manner to existing ID estimation methods: we find it performs comparable to older methods like local-PCA, also being relatively robust to noise, but that it does not match the best existing ID estimation methods.