Noise-Robust Deep Learning Network Training via Dual-SSNR Data
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Solution Overview
Problem
Deep neural networks, such as convolutional neural networks, lack robustness in recognizing objects in images with extremely noisy elements, which are common in real-world scenarios due to poor weather, sub-optimal lighting, or sub-optimal image acquisition conditions.
Innovation Solution
A noise-robust deep learning network is trained using a combination of noisy training images with low signal-to-combined-signal-and-noise ratio (SSNR) and noiseless images, where the noisy images are generated by reducing the dynamic range of source images and introducing noise, allowing the network to recognize objects in highly noisy conditions with performance comparable to human vision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks are trained with conventional noiseless images, then they achieve excellent performance in object classification and localization tasks, but they lack robustness in real-world applications with noisy images
Solution Approach 1:
The patent changes the training parameter from using only noiseless images to using images with varying noise levels. Specifically, the training set includes noiseless images and noisy images with different signal-to-noise ratios (SNRs), which forces the network to learn features that are robust to noise variations. This parameter change in the training data distribution directly addresses the robustness issue while maintaining versatility across different noise conditions.
Solution Approach 2:
The patent applies preliminary action by pre-training the network on noiseless images first, then fine-tuning it on noisy images with varying SNRs. This staged approach allows the network to first learn basic object representations from clean data, then progressively adapt to noisy conditions. The preliminary training on noiseless images establishes a strong foundation before introducing noise, preventing the network from being overwhelmed by noise during initial training.
2Reliability
If deep learning networks are trained only on noiseless images, then they achieve high accuracy in controlled environments, but they fail to perform reliably in real-world noisy conditions
Solution Approach 1:
The patent converts the harmful effect of noise into a beneficial training mechanism. Instead of treating noise as a disturbance to be avoided, the training process intentionally includes noisy images with varying SNRs, forcing the network to learn to distinguish meaningful object features from noise. This approach transforms noise sensitivity into noise robustness, allowing the network to thrive in real-world conditions where noise is inevitable.
Solution Approach 2:
The patent implements feedback by using the performance of the network on noisy test images to guide further training adjustments. The training process monitors how the network handles different noise levels and adjusts the training data distribution and network parameters accordingly. This feedback loop ensures the network continuously improves its ability to handle noise while maintaining accuracy on clean images.
3Reliability
If the training set includes only low SSNR noisy images, then the network becomes robust to noise, but it may lose accuracy on noiseless or high SSNR images
Solution Approach 1:
The patent applies preliminary action by first training the network on noiseless images to establish high accuracy on clean data, then progressively introducing noisy images with varying SNRs for fine-tuning. This staged approach ensures the network maintains its precision on clean images while learning robustness to noise. The preliminary training on noiseless images prevents the network from becoming overly sensitive to noise from the outset.
Solution Approach 2:
The patent applies local quality by treating different parts of the training data differently - noiseless images provide the baseline accuracy foundation, while noisy images with varying SNRs provide robustness training. The training process assigns different weights and roles to different types of training images, allowing the network to optimize both precision on clean images and robustness on noisy images simultaneously through differentiated learning experiences.
Data Source
AI summary
The exemplified methods and systems facilitate the training of a noise-robust deep learning network that is sufficiently robust in the recognition of objects in images having extremely noisy elements such that the noise-robust network can match, or exceed, the performance of human counterparts. The extremely noisy elements may correspond to extremely noisy viewing conditions, e.g., that often manifests themselves in the real-world as poor weather or environment conditions, sub-optimal lighting conditions, sub-optimal image acquisition or capture, etc. The noise-robust deep learning network is trained both (i) with noisy training images with low signal-to-combined-signal-and-noise ratio (SSNR) and (ii) either with noiseless, or generally noiseless, training images or a second set of noisy training images having a SSNR value greater than that of the low-SSNR noisy training images.