Neural Network Training via Variational Information Bottleneck
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Solution Overview
Problem
Existing neural networks face challenges in generalizing to new inputs and are vulnerable to adversarial attacks, with conventional training objectives leading to overfitting and poor robustness.
Innovation Solution
Training neural networks on a variational information bottleneck objective that encourages low mutual information between latent representations and inputs while maintaining high mutual information with target outputs, using a stochastic approach with noise sampling to optimize the objective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional training objectives are used, then the neural network can achieve good performance on training data, but the network overfits the training data and performs poorly on new inputs
Solution Approach 1:
The patent changes the training objective from conventional cross-entropy loss to a variational information bottleneck objective that includes a regularization term. This modifies the optimization parameters by adding a constraint that limits the mutual information between inputs and latent representations, thereby preventing overfitting while maintaining training performance and improving generalization to new inputs.
Solution Approach 2:
The patent introduces a latent representation layer as an intermediary between the input and output layers. This intermediate representation is constrained through the variational information bottleneck objective to capture only the most relevant features, acting as a mediator that filters out noise and redundant information, thus improving generalization performance.
2Productivity
If conventional training objectives are used, then the neural network can be trained efficiently, but the network becomes vulnerable to adversarial attacks
Solution Approach 1:
The patent modifies the training objective parameters by incorporating the variational information bottleneck term, which changes how the network learns features. This parameter change makes the learned representations more robust to perturbations, reducing adversarial vulnerability while maintaining training efficiency through gradient-based optimization.
Solution Approach 2:
The patent converts the harmful effect of overfitting (which makes networks vulnerable to adversarial attacks) into a benefit by using the same overfitting pressure to drive the discovery of more robust feature representations through the information bottleneck constraint. The regularization effect that prevents overfitting also inadvertently strengthens adversarial robustness.
3Measurement precision
If the neural network is trained to capture detailed input information, then the network output can be highly accurate, but the latent representation has high mutual information with the input reducing robustness
Solution Approach 1:
The patent changes the optimization objective to balance two competing goals: maximizing the mutual information between latent representations and target outputs (for accuracy) while minimizing the mutual information between latent representations and inputs (for robustness). This parameter change in the objective function creates a controlled information flow that maintains accuracy while reducing input dependency.
Solution Approach 2:
The patent applies different information preservation requirements to different parts of the network. The latent representation is designed to preserve information relevant to the target output while discarding information specific to the input. This local quality differentiation allows the network to be accurate for prediction while being robust to input variations.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes receiving training data; training a neural network on the training data, wherein the neural network is configured to: receive a network input, convert the network input into a latent representation of the network input, and process the latent representation to generate a network output from the network input, and wherein training the neural network on the training data comprises training the neural network on a variational information bottleneck objective that encourages, for each training input, the latent representation generated for the training input to have low mutual information with the training input while the network output generated for the training input has high mutual information with the target output for the training input.