Corruption Robustness via Fourier-Domain BN Adaptation
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Solution Overview
Problem
Deep neural networks (DNNs) perform poorly on corrupted data such as noisy or blurry images and lack robustness against various types of corruptions, requiring computationally expensive retraining or limited effectiveness in handling multiple corruption types.
Innovation Solution
A unified framework that includes a corruption-detection model and batch normalization (BN) statistics update, utilizing Fourier-domain transformations to identify corruption types and dynamically update BN statistics for improved robustness without extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is augmented with corrupted images to improve corruption robustness, then robustness against specific corruption types improves, but model accuracy on other corruption types degrades and retraining is computationally expensive
Solution Approach 1:
The patent pre-computes and stores correction factors for batch normalization statistics during training for various corruption types. During inference, when corruption is detected, the system simply retrieves and applies the pre-computed correction factors without retraining, thus improving robustness efficiently.
Solution Approach 2:
The patent dynamically adjusts batch normalization statistics parameters based on detected corruption types by applying pre-computed correction factors. This allows the model to adapt to different corruption conditions without retraining by changing the BN parameters rather than the entire model weights.
2Reliability
If batch normalization statistics are updated to adapt to a single corruption type, then robustness against that specific corruption improves, but the model cannot handle multiple different corruption types effectively
Solution Approach 1:
The patent segments the corruption handling by creating separate correction factor sets for different corruption types (e.g., Gaussian noise, salt-and-pepper noise, blur). The system detects the corruption type and applies the corresponding segment's correction factors, enabling multi-corruption handling without compromising performance on any single type.
Solution Approach 2:
The patent creates a universal batch normalization adaptation mechanism that can handle multiple corruption types through a single detection-and-correction framework. The same BN statistics update mechanism works across different corruption types by selecting appropriate pre-computed correction factors.
3Adaptability or versatility
If the model continuously updates BN statistics at inference time to adapt to ongoing corruption, then adaptability to changing corruption conditions improves, but computational overhead increases
Solution Approach 1:
The patent pre-computes correction factors during training for various corruption types and stores them for quick retrieval. During inference, the system only performs lightweight corruption detection and applies the pre-computed corrections, avoiding expensive continuous updates while maintaining adaptability.
Solution Approach 2:
The patent creates copies of batch normalization statistics for different corruption types during training. During inference, the system copies the appropriate pre-computed correction factors based on detected corruption type and applies them, avoiding the need for complex real-time computations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The framework enhances corruption accuracy by 8% on CIFAR10-C and 4% on ImageNet-C, maintaining robustness across multiple corruption types and adapting to changing corruption conditions without significant computational overhead.
Implementation Method 1
generating a frequency spectrum associated with the input data, wherein the generating includes creating the frequency spectrum by applying a frequency domain transformation on the input data
Data Source
AI summary
A computer-implemented method for training a machine-learning network. A computer-implemented method for training a machine-learning network includes generating a frequency spectrum associated with the input data, wherein the generating includes creating the frequency spectrum by applying a frequency domain transformation on the input data, normalizing the frequency spectrum to generate a normalized frequency spectrum, sending the normalized frequency spectrum to a hyper model configured classifying corruptions, utilizing the normalized frequency spectrum as input to the hyper model in order to classify a corruption associated with the input data, updating one or more weights associated with the classifier based on the corruption associated with the input data, and outputting a classification associated with the input data utilizing the classifier with updated weights.