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ML beginner seeks advice on VAE embedding space for variable image sizes

A user new to machine learning is seeking advice on improving their Variational Autoencoder (VAE) model. They are attempting to create an embedding space for an image dataset with varying spatial dimensions, which they cannot easily standardize. Their current method uses adaptive pooling to generate a fixed-dimensional latent representation, but the results are poor and the learned embeddings lack structure. AI

RANK_REASON User-generated content on a forum asking for help with a technical problem, not a news event.

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  1. r/MachineLearning TIER_1 English(EN) · /u/Few-Annual-157 ·

    Embedding space [D]

    <!-- SC_OFF --><div class="md"><p>Hello everyone,</p> <p>I’m relatively new to this area of machine learning and currently experimenting with Variational Autoencoders (VAEs) to build an embedding space for an image dataset with images have different spatial dimensions, I cannot e…