Model-Based Robust Deep Learning Against Natural Variation
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Solution Overview
Problem
Deep learning frameworks are fragile to natural variations such as lighting, weather conditions, and camera defects, which can significantly degrade the accuracy of trained neural networks, and existing adversarial training methods are inadequate in addressing these challenges.
Innovation Solution
A model-based robust deep learning approach that involves obtaining and utilizing models of natural variation to train neural networks to be robust against challenging natural conditions, using deep generative models to learn and represent these variations, and developing novel training algorithms to enhance the resilience of deep learning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard deep learning training is used, then training simplicity is maintained, but robustness to natural variation deteriorates
Solution Approach 1:
The patent applies preliminary action by obtaining a model of natural variation before training the neural network. This model characterizes how input data can be naturally varied by nuisance parameters, allowing the training process to proactively prepare for robustness against these variations rather than reacting to them during standard training.
Solution Approach 2:
The patent utilizes parameter changes by incorporating nuisance parameters into the training objective. These parameters represent natural variations in the input data, and by optimizing the neural network with respect to these parameters, the training process adapts to handle natural variation while maintaining a structured approach to complexity.
2Reliability
If robustness to natural variation is improved, then accuracy under challenging conditions improves, but training complexity increases
Solution Approach 1:
The patent introduces an intermediary element - the model of natural variation - that mediates between the raw input data and the neural network. This model acts as a bridge that captures the essence of natural variations without requiring the neural network to directly learn all possible variations, thereby improving robustness while managing complexity through this intermediate representation.
3Reliability
If existing adversarial training methods are used, then some robustness is achieved, but they are inadequate for natural variations
Solution Approach 1:
The patent applies dynamics by making the training approach adaptive to different types of natural variations. Rather than using a static adversarial training method, the system dynamically adjusts to the specific model of natural variation obtained, allowing it to adapt its robustness strategy to match the actual variations present in the data, thereby improving both robustness and applicability to natural conditions.
Data Source
AI summary
Methods, systems, and computer readable media for model-based robust deep learning. In some examples, a method includes obtaining a model of natural variation for a machine learning task. The model of natural variation includes a mapping that specifies how an input datum can be naturally varied by a nuisance parameter. The method includes training, using the model of natural variation and training data for the machine learning task, a neural network to complete the machine learning task such that the neural network is robust to natural variation specified by the model of natural variation.