  Estimating mutual correlations between random variables or data streams is
essential for intelligent behavior and decision-making. As a fundamental
quantity for measuring statistical relationships, mutual information has been
extensively studied and utilized for its generality and equitability. However,
existing methods often lack the efficiency needed for real-time applications,
such as test-time optimization of a neural network, or the differentiability
required for end-to-end learning, like histograms. We introduce a neural
network called InfoNet, which directly outputs mutual information estimations
of data streams by leveraging the attention mechanism and the computational
efficiency of deep learning infrastructures. By maximizing a dual formulation
of mutual information through large-scale simulated training, our approach
circumvents time-consuming test-time optimization and offers generalization
ability. We evaluate the effectiveness and generalization of our proposed
mutual information estimation scheme on various families of distributions and
applications. Our results demonstrate that InfoNet and its training process
provide a graceful efficiency-accuracy trade-off and order-preserving
properties. We will make the code and models available as a comprehensive
toolbox to facilitate studies in different fields requiring real-time mutual
information estimation.
