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A Perspective on Neural Capacity Estimation: Viability and Reliability

arXiv 2203.11793 ↗First posted 22 Mar 2022cs.IT cs.LG eess.SPcited by 0

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Abstract (arXiv)

Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks. These estimators ar referred to as neural mutual information estimation (NMIE)s. NMIEs differ from other approaches as they are data-driven estimators. As such, they have the potential to perform well on a large class of capacity problems. In order to test the performance across various NMIEs, it is desirable to establish a benchmark encompassing the different challenges of capacity estimation. This is the objective of this paper. In particular, we consider three scenarios for benchmarking:i the classic AWGN channel, ii channels continuous inputs optical intensity and peak-power constrained AWGN channel iii channels with a discrete output, i.e., Poisson channel. We also consider the extension to the multi-terminal case with iv the AWGN and optical MAC models. We argue that benchmarking a certain NMIE across these four scenarios provides a substantive test of performance. In this paper we study the performance of mutual information neural estimator (MINE), smoothed mutual information lower-bound estimator (SMILE), and directed information neural estimator (DINE). and provide insights on the performance of other methods as well. To summarize our benchmarking results, MINE provides the most reliable performance.

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17 of 55 references.

References · 17

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2018MINE: Mutual Information Neural Estimationcited by 680
  4. 2019On Variational Bounds of Mutual Informationcited by 550
  5. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 504
  6. 2019Understanding the Limitations of Variational Mutual Information Estimatorscited by 140
  7. 2019CCMI : Classifier based Conditional Mutual Information Estimationcited by 37
  8. 2018Model-free Training of End-to-end Communication Systemscited by 29
  9. 2021Neural Estimation of Statistical Divergencescited by 25
  10. 2019Deep Learning for Channel Coding via Neural Mutual Information Estimationcited by 14
  11. 2019Joint Learning of Geometric and Probabilistic Constellation Shapingcited by 13
  12. 2019Neural Entropic Estimation: A faster path to mutual information estimationcited by 9
5 more
  1. 2020Neural Estimators for Conditional Mutual Information Using Nearest Neighbors Samplingcited by 7
  2. 2020Capacity of Continuous Channels with Memory via Directed Information Neural Estimatorcited by 5
  3. 2021A Reverse Jensen Inequality Result with Application to Mutual Information Estimationcited by 5
  4. 2021Neural Capacity Estimators: How Reliable Are They?cited by 4
  5. 2020Computing the Feedback Capacity of Finite State Channels using Reinforcement Learningcited by 3

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