137 research outputs found
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
Representation disentanglement is an important goal of representation
learning that benefits various downstream tasks. To achieve this goal, many
unsupervised learning representation disentanglement approaches have been
developed. However, the training process without utilizing any supervision
signal have been proved to be inadequate for disentanglement representation
learning. Therefore, we propose a novel weakly-supervised training approach,
named as SW-VAE, which incorporates pairs of input observations as supervision
signals by using the generative factors of datasets. Furthermore, we introduce
strategies to gradually increase the learning difficulty during training to
smooth the training process. As shown on several datasets, our model shows
significant improvement over state-of-the-art (SOTA) methods on representation
disentanglement tasks
On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying structure
of the data generating process is considered to be of key importance in machine
learning. While disentangled representations were found to be useful for
diverse tasks such as abstract reasoning and fair classification, their
scalability and real-world impact remain questionable. We introduce a new
high-resolution dataset with 1M simulated images and over 1,800 annotated
real-world images of the same setup. In contrast to previous work, this new
dataset exhibits correlations, a complex underlying structure, and allows to
evaluate transfer to unseen simulated and real-world settings where the encoder
i) remains in distribution or ii) is out of distribution. We propose new
architectures in order to scale disentangled representation learning to
realistic high-resolution settings and conduct a large-scale empirical study of
disentangled representations on this dataset. We observe that disentanglement
is a good predictor for out-of-distribution (OOD) task performance.Comment: Published at ICLR 202
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
In deep learning, auxiliary objectives are often used to facilitate learning
in situations where data is scarce, or the principal task is extremely complex.
This idea is primarily inspired by the improved generalization capability
induced by solving multiple tasks simultaneously, which leads to a more robust
shared representation. Nevertheless, finding optimal auxiliary tasks that give
rise to the desired improvement is a crucial problem that often requires
hand-crafted solutions or expensive meta-learning approaches. In this paper, we
propose a novel framework, dubbed Detaux, whereby a weakly supervised
disentanglement procedure is used to discover new unrelated classification
tasks and the associated labels that can be exploited with the principal task
in any Multi-Task Learning (MTL) model. The disentanglement procedure works at
a representation level, isolating a subspace related to the principal task,
plus an arbitrary number of orthogonal subspaces. In the most disentangled
subspaces, through a clustering procedure, we generate the additional
classification tasks, and the associated labels become their representatives.
Subsequently, the original data, the labels associated with the principal task,
and the newly discovered ones can be fed into any MTL framework. Extensive
validation on both synthetic and real data, along with various ablation
studies, demonstrate promising results, revealing the potential in what has
been, so far, an unexplored connection between learning disentangled
representations and MTL. The code will be made publicly available upon
acceptance.Comment: Under review in Pattern Recognition Letter
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Representation learning assumes that real-world data is generated by a few
semantically meaningful generative factors (i.e., sources of variation) and
aims to discover them in the latent space. These factors are expected to be
causally disentangled, meaning that distinct factors are encoded into separate
latent variables, and changes in one factor will not affect the values of the
others. Compared to statistical independence, causal disentanglement allows
more controllable data generation, improved robustness, and better
generalization. However, most existing work assumes unconfoundedness in the
discovery process, that there are no common causes to the generative factors
and thus obtain only statistical independence. In this paper, we recognize the
importance of modeling confounders in discovering causal generative factors.
Unfortunately, such factors are not identifiable without proper inductive bias.
We fill the gap by introducing a framework entitled Confounded-Disentanglement
(C-Disentanglement), the first framework that explicitly introduces the
inductive bias of confounder via labels from domain expertise. In addition, we
accordingly propose an approach to sufficiently identify the causally
disentangled factors under any inductive bias of the confounder. We conduct
extensive experiments on both synthetic and real-world datasets. Our method
demonstrates competitive results compared to various SOTA baselines in
obtaining causally disentangled features and downstream tasks under domain
shifts.Comment: accepted to Neurips 202
Learning disentangled representations via product manifold projection
We propose a novel approach to disentangle the generative factors of
variation underlying a given set of observations. Our method builds upon the
idea that the (unknown) low-dimensional manifold underlying the data space can
be explicitly modeled as a product of submanifolds. This definition of
disentanglement gives rise to a novel weakly-supervised algorithm for
recovering the unknown explanatory factors behind the data. At training time,
our algorithm only requires pairs of non i.i.d. data samples whose elements
share at least one, possibly multidimensional, generative factor of variation.
We require no knowledge on the nature of these transformations, and do not make
any limiting assumption on the properties of each subspace. Our approach is
easy to implement, and can be successfully applied to different kinds of data
(from images to 3D surfaces) undergoing arbitrary transformations. In addition
to standard synthetic benchmarks, we showcase our method in challenging
real-world applications, where we compare favorably with the state of the art.Comment: 15 pages, 10 figure
Flow Factorized Representation Learning
A prominent goal of representation learning research is to achieve
representations which are factorized in a useful manner with respect to the
ground truth factors of variation. The fields of disentangled and equivariant
representation learning have approached this ideal from a range of
complimentary perspectives; however, to date, most approaches have proven to
either be ill-specified or insufficiently flexible to effectively separate all
realistic factors of interest in a learned latent space. In this work, we
propose an alternative viewpoint on such structured representation learning
which we call Flow Factorized Representation Learning, and demonstrate it to
learn both more efficient and more usefully structured representations than
existing frameworks. Specifically, we introduce a generative model which
specifies a distinct set of latent probability paths that define different
input transformations. Each latent flow is generated by the gradient field of a
learned potential following dynamic optimal transport. Our novel setup brings
new understandings to both \textit{disentanglement} and \textit{equivariance}.
We show that our model achieves higher likelihoods on standard representation
learning benchmarks while simultaneously being closer to approximately
equivariant models. Furthermore, we demonstrate that the transformations
learned by our model are flexibly composable and can also extrapolate to new
data, implying a degree of robustness and generalizability approaching the
ultimate goal of usefully factorized representation learning.Comment: NeurIPS2
CF-VAE: Causal Disentangled Representation Learning with VAE and Causal Flows
Learning disentangled representations is important in representation
learning, aiming to learn a low dimensional representation of data where each
dimension corresponds to one underlying generative factor. Due to the
possibility of causal relationships between generative factors, causal
disentangled representation learning has received widespread attention. In this
paper, we first propose new flows that can incorporate causal structure
information into the model, called causal flows. Based on the variational
autoencoders(VAE) commonly used in disentangled representation learning, we
design a new model, CF-VAE, which enhances the disentanglement ability of the
VAE encoder by utilizing the causal flows. By further introducing the
supervision of ground-truth factors, we demonstrate the disentanglement
identifiability of our model. Experimental results on both synthetic and real
datasets show that CF-VAE can achieve causal disentanglement and perform
intervention experiments. Moreover, CF-VAE exhibits outstanding performance on
downstream tasks and has the potential to learn causal structure among factors.Comment: 12 pages, 7 figure
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