Forward Operator Estimation in Generative Models with Kernel Transfer Operators

• URL: http://arxiv.org/abs/2112.00305v1
• Date: Wed, 1 Dec 2021 06:54:31 GMT
• Title: Forward Operator Estimation in Generative Models with Kernel Transfer Operators
• Authors: Zhichun Huang and Rudrasis Chakraborty and Vikas Singh
• Abstract summary: We show that our formulation enables highly efficient distribution approximation and sampling, and offers surprisingly good empirical performance. We also show that the algorithm also performs well in small sample size settings (in brain imaging)
• Score: 37.999297683250575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models which use explicit density modeling (e.g., variational autoencoders, flow-based generative models) involve finding a mapping from a known distribution, e.g. Gaussian, to the unknown input distribution. This often requires searching over a class of non-linear functions (e.g., representable by a deep neural network). While effective in practice, the associated runtime/memory costs can increase rapidly, usually as a function of the performance desired in an application. We propose a much cheaper (and simpler) strategy to estimate this mapping based on adapting known results in kernel transfer operators. We show that our formulation enables highly efficient distribution approximation and sampling, and offers surprisingly good empirical performance that compares favorably with powerful baselines, but with significant runtime savings. We show that the algorithm also performs well in small sample size settings (in brain imaging).

Related papers

• Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows [54.050498411883495]
  We develop a novel training method for generative models, such as Generative Adversarial Networks and Normalizing Flows. We show that achieving a specified precision-recall trade-off corresponds to minimizing a unique $f$-divergence from a family we call the textitPR-divergences. Our approach improves the performance of existing state-of-the-art models like BigGAN in terms of either precision or recall when tested on datasets such as ImageNet.
  arXiv  Detail & Related papers  (2023-05-30T10:07:17Z)
• Using Intermediate Forward Iterates for Intermediate Generator Optimization [14.987013151525368]
  Intermediate Generator Optimization can be incorporated into any standard autoencoder pipeline for the generative task. We show applications of the IGO on two dense predictive tasks viz., image extrapolation, and point cloud denoising.
  arXiv  Detail & Related papers  (2023-02-05T08:46:15Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Exponentially Tilted Gaussian Prior for Variational Autoencoder [3.52359746858894]
  Recent studies show that probabilistic generative models can perform poorly on this task. We propose the exponentially tilted Gaussian prior distribution for the Variational Autoencoder (VAE) We show that our model produces high quality image samples which are more crisp than that of a standard Gaussian VAE.
  arXiv  Detail & Related papers  (2021-11-30T18:28:19Z)
• Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models [7.217783736464403]
  Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space. We propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and the weights of a neural network surrogate model.
  arXiv  Detail & Related papers  (2021-08-28T14:31:45Z)
• Continual Learning with Fully Probabilistic Models [70.3497683558609]
  We present an approach for continual learning based on fully probabilistic (or generative) models of machine learning. We propose a pseudo-rehearsal approach using a Gaussian Mixture Model (GMM) instance for both generator and classifier functionalities. We show that GMR achieves state-of-the-art performance on common class-incremental learning problems at very competitive time and memory complexity.
  arXiv  Detail & Related papers  (2021-04-19T12:26:26Z)
• Attentive Gaussian processes for probabilistic time-series generation [4.94950858749529]
  We propose a computationally efficient attention-based network combined with the Gaussian process regression to generate real-valued sequence. We develop a block-wise training algorithm to allow mini-batch training of the network while the GP is trained using full-batch. The algorithm has been proved to converge and shows comparable, if not better, quality of the found solution.
  arXiv  Detail & Related papers  (2021-02-10T01:19:15Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Gaussianization Flows [113.79542218282282]
  We propose a new type of normalizing flow model that enables both efficient iteration of likelihoods and efficient inversion for sample generation. Because of this guaranteed expressivity, they can capture multimodal target distributions without compromising the efficiency of sample generation.
  arXiv  Detail & Related papers  (2020-03-04T08:15:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.