Transforming auto-encoders
… transforming auto-encoder direct knowledge of the transformation, we can model the small
transformations … This reduces the transforming auto-encoder to a much less powerful learning …
transformations … This reduces the transforming auto-encoder to a much less powerful learning …
Quantised transforming auto-encoders: Achieving equivariance to arbitrary transformations in deep networks
J Jiao, JF Henriques - arXiv preprint arXiv:2111.12873, 2021 - arxiv.org
… They extend auto-encoders with an equivariant latent code, … its equivariance wrt example
transformations. A single capsule … Transforming auto-encoders. In International conference on …
transformations. A single capsule … Transforming auto-encoders. In International conference on …
Stacked what-where auto-encoders
We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which
integrates discriminative and generative pathways and provides a unified approach to …
integrates discriminative and generative pathways and provides a unified approach to …
Auto-encoders in deep learning—a review with new perspectives
S Chen, W Guo - Mathematics, 2023 - mdpi.com
… auto-encoders from three different perspectives. We also discuss the relationships between
auto-encoders, … Then, we focus on the available toolkits for auto-encoders. Finally, this paper …
auto-encoders, … Then, we focus on the available toolkits for auto-encoders. Finally, this paper …
Improving variational auto-encoders using householder flow
JM Tomczak, M Welling - arXiv preprint arXiv:1611.09630, 2016 - arxiv.org
… Variational auto-encoders (VAE) are scalable and powerful … , ie, a series of invertible
transformations to latent variables with a … transformations that we refer to as the Householder flow. …
transformations to latent variables with a … transformations that we refer to as the Householder flow. …
Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data
… to transform images, we seek to train auto-encoders that … transformed images. We believe
as long as the trained features are sufficiently informative, we can decode the transformations …
as long as the trained features are sufficiently informative, we can decode the transformations …
Fast transformation of discriminators into encoders using pre-trained GANs
… GANs can be treated as a special type of auto-encoders, and various auto-encoders are
widely used for computer vision tasks, such as face images [10], [11], animations [12] and …
widely used for computer vision tasks, such as face images [10], [11], animations [12] and …
[PDF][PDF] Quantised transforming auto-encoders
J Jiao, JF Henriques - 2021 - research.birmingham.ac.uk
… transformations in deep networks, purely from data, without being given a model of those
transformations. … equivariant to image translation, a transformation that can be easily modelled (…
transformations. … equivariant to image translation, a transformation that can be easily modelled (…
How auto-encoders could provide credit assignment in deep networks via target propagation
Y Bengio - arXiv preprint arXiv:1407.7906, 2014 - arxiv.org
… Have one stack of auto-encoders for each modality, used to transform each modalities data
X(t) into a representation where the marginal P(X(t)) can be captured easily through some P(…
X(t) into a representation where the marginal P(X(t)) can be captured easily through some P(…
Self-supervised variational auto-encoders
I Gatopoulos, JM Tomczak - Entropy, 2021 - mdpi.com
… utilizes deterministic and discrete transformations of data. This … transformation as a latent
variable, where the transformation … architecture, ie, multiple transformations, and we show its …
variable, where the transformation … architecture, ie, multiple transformations, and we show its …