You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper, we provide an information-theoretic interpretation of the Vector Quantized-Variational Autoencoder (VQ-VAE). We show that the loss function of the original VQ-VAE can be derived from the variational deterministic information bottleneck (VDIB) principle. On the other hand, the VQ-VAE trained by the Expectation Maximization (EM) algorithm can be viewed as an approximation to the variational information bottleneck(VIB) principle.

Cited Abstract(s):
[1]: We present a variational approximation to the information bottleneck of (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method "Deep Variational Information Bottleneck", or Deep VIB. We show that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack.

[2]: Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms.

[3]: We define the relevant information in a signal @math as being the information that this signal provides about another signal @math . Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal @math requires more than just predicting @math , it also requires specifying which features of @math play a role in the prediction. We formalize this problem as that of finding a short code for @math that preserves the maximum information about @math . That is, we squeeze the information that @math provides about @math through a bottleneck' formed by a limited set of codewords @math . This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure @math emerges from the joint statistics of @math and @math . This approach yields an exact set of self consistent equations for the coding rules @math and @math . Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.
Given a joint probability distribution @math of input data @math and the observed relevant random variable @math , the information bottleneck (IB) method seeks a representation @math such that the mutual information @math is minimized, while preserving the mutual information @math [3] . @math can be seen as a measure of the predicative power of @math on @math , and @math can be seen as a compression measure. Hence, the information bottleneck is designed to find the trade off between the accuracy and compression. [2] first used the information bottleneck principle to analysis the deep neural networks theoretically, but no practical models are derived from the IB model. [1] presents a variational approximation to the information bottleneck so that the IB-based models can be parameterized by the neural networks.