29 Oct,2023
Masked images can still be vulnerable to adversarial attacks, where small, imperceptible changes can lead to misclassification or other unintended outcomes.
Sensitive information can sometimes be leaked through masked regions if the model is not designed carefully. For example, reconstructing masked faces might reveal identity.
Biases present in the training data can be amplified when using masked images. For instance, if the training data has a gender bias, masked models may perpetuate that bias.
Masked images can lead to models that are overfitted to specific types of occlusions or alterations, making them less robust in real-world scenarios.
Masked images might not accurately represent the complexity and diversity of real-world data, potentially limiting a model's generalization.
Creating high-quality masked images for training can be challenging, as manually creating masks can introduce inconsistencies and biases.
There are ethical concerns related to the use of masked images, such as privacy implications and the potential for misuse in deepfake generation.
Masked modeling can be computationally expensive, as it requires additional processing to generate and apply masks during both training and inference.
By masking parts of training data, the effective amount of data for learning may be reduced, potentially affecting model performance.