Unsupervised Dynamic Feature Selection for Robust Latent Spaces in Vision Tasks
This addresses robustness issues in latent spaces for vision models, offering a broadly applicable solution without labeled data, though it appears incremental as it builds on existing feature selection techniques.
The paper tackles the problem of noisy or irrelevant features degrading latent representations in vision tasks by proposing an unsupervised Dynamic Feature Selection (DFS) method, which improves generalization performance in clustering and image generation with minimal computational overhead.
Latent representations are critical for the performance and robustness of machine learning models, as they encode the essential features of data in a compact and informative manner. However, in vision tasks, these representations are often affected by noisy or irrelevant features, which can degrade the model's performance and generalization capabilities. This paper presents a novel approach for enhancing latent representations using unsupervised Dynamic Feature Selection (DFS). For each instance, the proposed method identifies and removes misleading or redundant information in images, ensuring that only the most relevant features contribute to the latent space. By leveraging an unsupervised framework, our approach avoids reliance on labeled data, making it broadly applicable across various domains and datasets. Experiments conducted on image datasets demonstrate that models equipped with unsupervised DFS achieve significant improvements in generalization performance across various tasks, including clustering and image generation, while incurring a minimal increase in the computational cost.