insight - Computer Vision - # Biologically-inspired CNN Front-Ends for Improved Robustness

Improving Convolutional Neural Network Robustness to Common Image Corruptions by Simulating Early Visual Processing

Q: How would the robustness gains scale to larger input image sizes and different out-of-domain datasets?

The robustness gains observed in the novel CNN families, RetinaNets and EVNets, may scale positively to larger input image sizes due to the multi-stage front-end architecture that simulates early visual processing. Larger images could provide more detailed spatial information, allowing the RetinaBlock and VOneBlock to extract richer features that enhance robustness against common corruptions. The increased resolution may also improve the model's ability to generalize across various datasets, as the biologically-inspired components are designed to mimic the hierarchical processing of visual information akin to the primate visual system. However, the effectiveness of these robustness gains on out-of-domain datasets would depend on the similarity of the new datasets to the training data. If the new datasets exhibit significant differences in distribution or corruption types, the models may not perform as well. Future research should focus on evaluating these models on diverse out-of-domain datasets to assess their generalization capabilities. Additionally, exploring how the architecture adapts to different input sizes and the impact of varying corruption types will be crucial in understanding the scalability of robustness gains.

Q: What are the individual contributions of the different components within the RetinaBlock in improving model robustness?

The RetinaBlock comprises several key components that contribute to its overall effectiveness in enhancing model robustness. These include: Light-Adaptation Layer: This component normalizes the input based on local mean luminance, which helps the model maintain performance across varying lighting conditions. By adjusting for luminance, the model becomes less sensitive to changes in brightness, thereby improving robustness against brightness-related corruptions. Difference-of-Gaussian (DoG) Convolutional Layer: The DoG layer simulates the spatial summation and center-surround antagonism characteristic of retinal ganglion cells. This layer enhances feature selectivity by emphasizing edges and contours, which are critical for object recognition. The band-pass filtering effect of the DoG layer helps the model focus on relevant features while suppressing noise, contributing to improved robustness against various corruptions. Contrast-Normalization Layer: This layer mimics the adaptive processes observed in the lateral geniculate nucleus (LGN) by normalizing activations based on local contrast. By adjusting the model's sensitivity to contrast variations, this component enhances the model's ability to handle contrast-related corruptions, thereby improving overall robustness. Parallel Pathways for Midget and Parasol Cells: The RetinaBlock processes inputs through separate pathways for midget and parasol cells, which allows the model to capture both color-opponent and achromatic information. This dual processing enhances the model's ability to generalize across different types of visual stimuli, contributing to robustness against a wider range of corruptions. Together, these components create a multi-faceted approach to early visual processing, allowing the RetinaBlock to effectively enhance the robustness of CNNs against common image corruptions while maintaining a degree of biological plausibility.

Core Concepts

Incorporating biologically-inspired front-end blocks that simulate retinal and primary visual cortex processing can improve the robustness of convolutional neural networks to common image corruptions.

Abstract

The authors introduce two novel CNN model families, RetinaNets and EVNets, that incorporate biologically-inspired front-end blocks to improve model robustness to common image corruptions.

The RetinaBlock simulates key features of retinal and lateral geniculate nucleus (LGN) processing, including spatial summation, center-surround antagonism, light adaptation, and contrast normalization. RetinaNets integrate the RetinaBlock with a standard CNN back-end, while EVNets couple the RetinaBlock with the previously proposed VOneBlock (simulating primary visual cortex) before the back-end.

Experiments on the Tiny ImageNet dataset show that both RetinaNets and EVNets exhibit improved robustness to a wide range of common corruptions compared to the base CNN models, with EVNets providing the largest gains. The improvements are observed across different CNN architectures (ResNet18 and VGG16).

The authors find that the RetinaBlock and VOneBlock contribute complementary forms of invariance, leading to cumulative robustness benefits when combined in the EVNet architecture. While the biologically-inspired front-ends slightly decrease clean image accuracy, the overall robustness improvements demonstrate the value of incorporating early visual processing mechanisms into deep learning models.

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Stats

"Parasol cells exhibit a higher initial slope and a higher degree of contrast saturation than midget cells, whereas midget cells have a higher half-response constant."
"The SF response curve of RetinaBlock cells delineates a DoG spectra, consonant with empirical measurements of retinal ganglion cell responses."
"RetinaResNet18 achieves an overall relative gain of 12.7% in mean accuracy on the Tiny ImageNet-C dataset compared to the base ResNet18 model."
"EVResNet18 improves robustness across all corruption categories, with an overall relative gain of 18.1%."

Quotes

"Explicitly modeling pre-cortical vision with a neuro-inspired front-end improves CNN robustness."
"The cumulative VOneBlock and RetinaBlock gains indicate that these blocks contribute to different types of invariance, yielding stacked gains in model robustness."

Key Insights Distilled From

Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness

by Lucas Piper,... at arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16838.pdf

Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness

Deeper Inquiries

How would the robustness gains scale to larger input image sizes and different out-of-domain datasets?

The robustness gains observed in the novel CNN families, RetinaNets and EVNets, may scale positively to larger input image sizes due to the multi-stage front-end architecture that simulates early visual processing. Larger images could provide more detailed spatial information, allowing the RetinaBlock and VOneBlock to extract richer features that enhance robustness against common corruptions. The increased resolution may also improve the model's ability to generalize across various datasets, as the biologically-inspired components are designed to mimic the hierarchical processing of visual information akin to the primate visual system.
However, the effectiveness of these robustness gains on out-of-domain datasets would depend on the similarity of the new datasets to the training data. If the new datasets exhibit significant differences in distribution or corruption types, the models may not perform as well. Future research should focus on evaluating these models on diverse out-of-domain datasets to assess their generalization capabilities. Additionally, exploring how the architecture adapts to different input sizes and the impact of varying corruption types will be crucial in understanding the scalability of robustness gains.

What are the individual contributions of the different components within the RetinaBlock in improving model robustness?

The RetinaBlock comprises several key components that contribute to its overall effectiveness in enhancing model robustness. These include:

Light-Adaptation Layer: This component normalizes the input based on local mean luminance, which helps the model maintain performance across varying lighting conditions. By adjusting for luminance, the model becomes less sensitive to changes in brightness, thereby improving robustness against brightness-related corruptions.

Difference-of-Gaussian (DoG) Convolutional Layer: The DoG layer simulates the spatial summation and center-surround antagonism characteristic of retinal ganglion cells. This layer enhances feature selectivity by emphasizing edges and contours, which are critical for object recognition. The band-pass filtering effect of the DoG layer helps the model focus on relevant features while suppressing noise, contributing to improved robustness against various corruptions.

Contrast-Normalization Layer: This layer mimics the adaptive processes observed in the lateral geniculate nucleus (LGN) by normalizing activations based on local contrast. By adjusting the model's sensitivity to contrast variations, this component enhances the model's ability to handle contrast-related corruptions, thereby improving overall robustness.

Parallel Pathways for Midget and Parasol Cells: The RetinaBlock processes inputs through separate pathways for midget and parasol cells, which allows the model to capture both color-opponent and achromatic information. This dual processing enhances the model's ability to generalize across different types of visual stimuli, contributing to robustness against a wider range of corruptions.

Together, these components create a multi-faceted approach to early visual processing, allowing the RetinaBlock to effectively enhance the robustness of CNNs against common image corruptions while maintaining a degree of biological plausibility.

How could the incorporation of independent neural noise in the different neurobiological stages further shape a more robust network while mimicking the inherent variability in primate visual systems?

Incorporating independent neural noise at various neurobiological stages within the CNN architecture could significantly enhance model robustness by mimicking the inherent variability observed in primate visual systems. This approach could yield several benefits:

Increased Generalization: By introducing noise, the model can learn to be less reliant on precise input features, thereby improving its ability to generalize across different datasets and conditions. This mimics the natural variability in sensory processing, where neurons exhibit stochastic firing patterns that contribute to robust perception.

Enhanced Robustness to Corruptions: Neural noise can act as a form of regularization, helping the model to become more resilient to perturbations and corruptions. By training with noise, the model learns to focus on the underlying structure of the data rather than being overly sensitive to specific input variations, which is crucial for real-world applications where data may be corrupted or distorted.

Improved Feature Learning: The introduction of noise can encourage the model to explore a broader range of feature representations, leading to a more diverse set of learned features. This diversity can enhance the model's ability to recognize objects under varying conditions, thereby improving robustness.

Mimicking Biological Variability: Primate visual systems exhibit variability in neural responses due to factors such as synaptic noise and intrinsic neuronal variability. By incorporating similar noise mechanisms in the CNN architecture, the model can better replicate biological processing, potentially leading to improved performance in tasks that require adaptability and robustness.

In summary, the strategic incorporation of independent neural noise at different stages of processing could create a more robust network that not only performs better under various conditions but also aligns more closely with the biological principles of visual processing in primates. This approach could pave the way for developing more resilient AI systems capable of operating effectively in dynamic and unpredictable environments.