Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boxhead: A Dataset for Learning Hierarchical Representations   Disentanglement is hypothesized to be beneficial towards a number of
downstream tasks. However, a common assumption in learning disentangled
representations is that the data generative factors are statistically
independent. As current methods are almost solely evaluated on toy datasets
where this ideal assumption holds, we investigate their performance in
hierarchical settings, a relevant feature of real-world data. In this work, we
introduce Boxhead, a dataset with hierarchically structured ground-truth
generative factors. We use this novel dataset to evaluate the performance of
state-of-the-art autoencoder-based disentanglement models and observe that
hierarchical models generally outperform single-layer VAEs in terms of
disentanglement of hierarchically arranged factors.

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The categories of this paper are: astro-ph
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The categories of this paper are: math
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat