A Comprehensive Survey on Enhancing Computer Vision Model Robustness against Common Corruptions

Core Concepts

To improve the robustness of computer vision models against common corruptions, methods such as data augmentation, representation learning, knowledge distillation, and network components are employed. These approaches aim to enhance model generalization and reliability in real-world scenarios.

Abstract

The survey explores methods to enhance the robustness of computer vision models against common corruptions. It categorizes approaches into data augmentation, representation learning, knowledge distillation, and network components. These strategies aim to improve model performance under unexpected changes in input images. The content discusses various techniques such as basic and advanced data augmentations, contrastive learning for robust representations, disentanglement learning for invariant information extraction, and knowledge distillation from teacher models to students. Additionally, it covers network component modifications like receptive field adjustments and normalization layers for improved model resilience. Furthermore, attention mechanisms in transformers are explored for their impact on model robustness against common corruptions. The study highlights the importance of patch size in Vision Transformers (ViTs) and the benefits of patchifying images for enhancing corruption robustness in CNNs.

Stats

Adversarial noise is visually imperceptible noise that impairs classification performance. Common corruptions include distortions like noise, blur, or digital transformations. ImageNet-C contains 15 corruptions with five severity levels each. CIFAR-C includes 19 synthetic corruptions. Mixup generates images by interpolating linearly two source images. Self-supervised contrastive learning enforces a model to learn similar latent representations for source images and their augmented versions. NoisyStudent trains models by semi-supervised learning using pseudo labels from a teacher model. Batch Normalization improves DNNs' robustness towards image corruptions by rectifying statistics according to new test data.

Quotes

"Mixup generates images by interpolating linearly two source images." "Self-supervised contrastive learning forces a model to learn similar latent representations for source images and their augmented versions." "NoisyStudent trains models by semi-supervised learning using pseudo labels from a teacher model."

Key Insights Distilled From

A Survey on the Robustness of Computer Vision Models against Common Corruptions

by Shunxin Wang... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2305.06024.pdf

Deeper Inquiries

How do adversarial training methods impact the overall performance of computer vision models

Adversarial training methods have a significant impact on the overall performance of computer vision models by improving their robustness against adversarial attacks and common image corruptions. By training models to classify images correctly even in the presence of imperceptible perturbations, adversarial training helps enhance the model's ability to generalize well and make accurate predictions in real-world scenarios. It forces the model to learn more robust features that are invariant to small changes in input data, thus reducing the risk of misclassification due to noise or distortions.

What are the potential drawbacks of relying heavily on data augmentation techniques for improving corruption robustness

While data augmentation techniques can be effective in improving corruption robustness, there are potential drawbacks associated with relying heavily on them. One drawback is that excessive augmentation may lead to overfitting, where the model becomes too specialized on augmented data and fails to generalize well on unseen or real-world data. Moreover, augmentations that do not accurately represent real-world variations can introduce biases into the model and affect its performance when deployed in practical applications. Additionally, selecting inappropriate augmentations or applying them incorrectly can degrade rather than improve model performance.

How can disentanglement learning be further optimized to extract more meaningful information from image representations

To optimize disentanglement learning for extracting more meaningful information from image representations, several strategies can be implemented: Improved Separation: Enhance the separation between content codes (semantic information) and style codes (appearance information) by designing better architectures or loss functions that encourage clear distinction between these two components. Domain-Specific Disentanglement: Tailor disentanglement learning specifically for different domains or types of corruptions by adjusting hyperparameters or introducing domain-specific constraints during training. Regularization Techniques: Incorporate regularization techniques such as sparsity constraints or mutual information maximization to ensure that each code captures distinct aspects of an image without overlap. Adaptive Learning Rates: Implement adaptive learning rates for different components (content vs style) based on their importance in downstream tasks, allowing for more focused optimization during training. Evaluation Metrics: Develop new evaluation metrics tailored towards assessing how effectively disentangled representations capture relevant semantic information while filtering out irrelevant details introduced by corruptions. By implementing these optimizations, disentanglement learning can extract more informative and reliable features from image representations, leading to enhanced corruption robustness in computer vision models.