Variable Length Embeddings
Johnathan Chiu Andi Gu Matt Zhou
In this work, we introduce a novel deep learning architecture, Variable
Length Embeddings (VLEs), an autoregressive model that can produce a latent
representation composed of an arbitrary number of tokens. As a proof of
concept, we demonstrate the capabilities of VLEs on tasks that involve
reconstruction and image decomposition. We evaluate our experiments on a mix of
the iNaturalist and ImageNet datasets and find that VLEs achieve comparable
reconstruction results to a state of the art VAE, using less than a tenth of
the parameters.