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Abstract. This paper evaluates how autoencoder variants with di erent architectures and parameter settings a ect the quality of 2D projections for spatial ensembles, and proposes a guided selection approach based on partially labeled data. Extracting features with autoencoders prior to applying techniques like UMAP substantially enhances the. TLDR; What you can find here: A working VAE (variational auto-encoder) example on PyTorch with a lot of flags (both FC and FCN, as well as a number of failed experiments);; Some tests - which loss works best (I did not do proper scaling, but out-of-the-box BCE works best compared to SSIM and MSE);; Some experimental boilerplate code for beginners on PyTorch 0.4 (I tried various architecture.

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