# Mutual Information Gradient Estimation for Representation Learning

@article{Wen2020MutualIG, title={Mutual Information Gradient Estimation for Representation Learning}, author={Liangjiang Wen and Yiji Zhou and Lirong He and Mingyuan Zhou and Zenglin Xu}, journal={ArXiv}, year={2020}, volume={abs/2005.01123} }

Mutual information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances establish tractable and scalable MI estimators to discover useful representation. However, most of existing methods are not capable of providing accurate estimation of MI with low-variance when the MI is large. We argue that estimating gradients of MI is more appealing for representation learning than directly…

## 16 Citations

Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks

- Computer ScienceInf. Sci.
- 2021

Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization

- Computer ScienceArXiv
- 2021

This work revisits the mathematics of popular variational MI bounds from the lens of unnormalized statistical modeling and convex optimization, and results in a novel, simple, and powerful contrastive MI estimator, named FLO.

Combating the Instability of Mutual Information-based Losses via Regularization

- Computer Science
- 2020

This work identifies the symptoms behind MI-based losses' instability and mitigates both issues by adding a novel regularization term to the existing losses, and theoretically and experimentally demonstrates that added regularization stabilizes training.

Nonparametric Score Estimators

- Mathematics, Computer ScienceICML
- 2020

This work proposes score estimators based on iterative regularization that enjoy computational benefits from curl-free kernels and fast convergence and provides a unifying view of these estimators under the framework of regularized nonparametric regression.

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

- Computer ScienceICML
- 2020

This paper proposes the amortized residual denoising autoencoder (AR-DAE) to approximate the gradient of the log density function, which can be used to estimate thegradient of entropy.

Self-Supervision Can Be a Good Few-Shot Learner

- Computer ScienceArXiv
- 2022

This work proposes an effective unsupervised FSL method, learning representations with self-supervision, following the InfoMax principle, which achieves comparable performance on widely used FSL benchmarks without any labels of the base classes.

Barycentric-alignment and invertibility for domain generalization

- Computer Science
- 2021

A new upper bound is derived for Domain Generalization (DG) problem, where the hypotheses are composed of a common representation mapping followed by a labeling function, by imposing mild assumptions on the loss function and an invertibility requirement on the representation map when restricted to the low-dimensional data manifold.

Neural Approximate Sufficient Statistics for Implicit Models

- Computer ScienceICLR
- 2021

We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of likelihood function is intractable but sampling /…

Barycenteric distribution alignment and manifold-restricted invertibility for domain generalization

- Computer ScienceArXiv
- 2021

A new representation learning cost for DG is motivated that additively balances three competing objectives: 1) minimizing classification error across seen domains via cross entropy, 2) enforcing domain-invariance in the representation space via the Wasserstein-2 barycenter cost, and 3) promoting non-degenerate, nearly-invertible representation via one of two mechanisms.

Cross-modal Image Retrieval with Deep Mutual Information Maximization

- Computer ScienceNeurocomputing
- 2022

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