2,409 research outputs found
Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learning a
distribution that faithfully recovers a reference distribution in its entirety.
However, in some cases, we may want to only learn some aspects (e.g., cluster
or manifold structure), while modifying others (e.g., style, orientation or
dimension). In this work, we propose an approach to learn generative models
across such incomparable spaces, and demonstrate how to steer the learned
distribution towards target properties. A key component of our model is the
Gromov-Wasserstein distance, a notion of discrepancy that compares
distributions relationally rather than absolutely. While this framework
subsumes current generative models in identically reproducing distributions,
its inherent flexibility allows application to tasks in manifold learning,
relational learning and cross-domain learning.Comment: International Conference on Machine Learning (ICML
Aligning Time Series on Incomparable Spaces
Dynamic time warping (DTW) is a useful method for aligning, comparing and
combining time series, but it requires them to live in comparable spaces. In
this work, we consider a setting in which time series live on different spaces
without a sensible ground metric, causing DTW to become ill-defined. To
alleviate this, we propose Gromov dynamic time warping (GDTW), a distance
between time series on potentially incomparable spaces that avoids the
comparability requirement by instead considering intra-relational geometry. We
demonstrate its effectiveness at aligning, combining and comparing time series
living on incomparable spaces. We further propose a smoothed version of GDTW as
a differentiable loss and assess its properties in a variety of settings,
including barycentric averaging, generative modeling and imitation learning
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a
learned dynamics model, with ambiguous visual information, from a learned
vision model, in the context of interacting agents. Our method is based on a
graph-structured variational recurrent neural network (Graph-VRNN), which is
trained end-to-end to infer the current state of the (partially observed)
world, as well as to forecast future states. We show that our method
outperforms various baselines on two sports datasets, one based on real
basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read
Heterogeneous Wasserstein Discrepancy for Incomparable Distributions
Optimal Transport (OT) metrics allow for defining discrepancies between two
probability measures. Wasserstein distance is for longer the celebrated
OT-distance frequently-used in the literature, which seeks probability
distributions to be supported on the metric space. Because of
its high computational complexity, several approximate Wasserstein distances
have been proposed based on entropy regularization or on slicing, and
one-dimensional Wassserstein computation. In this paper, we propose a novel
extension of Wasserstein distance to compare two incomparable distributions,
that hinges on the idea of , embeddings, and
on computing the closed-form Wassertein distance between the sliced
distributions. We provide a theoretical analysis of this new divergence, called
, and we show that it
preserves several interesting properties including rotation-invariance. We show
that the embeddings involved in HWD can be efficiently learned. Finally, we
provide a large set of experiments illustrating the behavior of HWD as a
divergence in the context of generative modeling and in query framework
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Factor analysis aims to determine latent factors, or traits, which summarize
a given data set. Inter-battery factor analysis extends this notion to multiple
views of the data. In this paper we show how a nonlinear, nonparametric version
of these models can be recovered through the Gaussian process latent variable
model. This gives us a flexible formalism for multi-view learning where the
latent variables can be used both for exploratory purposes and for learning
representations that enable efficient inference for ambiguous estimation tasks.
Learning is performed in a Bayesian manner through the formulation of a
variational compression scheme which gives a rigorous lower bound on the log
likelihood. Our Bayesian framework provides strong regularization during
training, allowing the structure of the latent space to be determined
efficiently and automatically. We demonstrate this by producing the first (to
our knowledge) published results of learning from dozens of views, even when
data is scarce. We further show experimental results on several different types
of multi-view data sets and for different kinds of tasks, including exploratory
data analysis, generation, ambiguity modelling through latent priors and
classification.Comment: 49 pages including appendi
Auto-regressive Image Synthesis with Integrated Quantization
Deep generative models have achieved conspicuous progress in realistic image
synthesis with multifarious conditional inputs, while generating diverse yet
high-fidelity images remains a grand challenge in conditional image generation.
This paper presents a versatile framework for conditional image generation
which incorporates the inductive bias of CNNs and powerful sequence modeling of
auto-regression that naturally leads to diverse image generation. Instead of
independently quantizing the features of multiple domains as in prior research,
we design an integrated quantization scheme with a variational regularizer that
mingles the feature discretization in multiple domains, and markedly boosts the
auto-regressive modeling performance. Notably, the variational regularizer
enables to regularize feature distributions in incomparable latent spaces by
penalizing the intra-domain variations of distributions. In addition, we design
a Gumbel sampling strategy that allows to incorporate distribution uncertainty
into the auto-regressive training procedure. The Gumbel sampling substantially
mitigates the exposure bias that often incurs misalignment between the training
and inference stages and severely impairs the inference performance. Extensive
experiments over multiple conditional image generation tasks show that our
method achieves superior diverse image generation performance qualitatively and
quantitatively as compared with the state-of-the-art.Comment: Accepted to ECCV 2022 as Oral Presentatio
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