Robust Partial-Label Learning by Leveraging Class Activation Values
This work addresses robustness issues in partial-label learning for real-world applications with noisy annotations, representing an incremental improvement over existing methods.
The paper tackles the problem of partial-label learning with noisy training data by proposing a method based on subjective logic and class activation values, resulting in more robust predictions under high noise levels, out-of-distribution data, and adversarial perturbations.
Real-world training data is often noisy; for example, human annotators assign conflicting class labels to the same instances. Partial-label learning (PLL) is a weakly supervised learning paradigm that allows training classifiers in this context without manual data cleaning. While state-of-the-art methods have good predictive performance, their predictions are sensitive to high noise levels, out-of-distribution data, and adversarial perturbations. We propose a novel PLL method based on subjective logic, which explicitly represents uncertainty by leveraging the magnitudes of the underlying neural network's class activation values. Thereby, we effectively incorporate prior knowledge about the class labels by using a novel label weight re-distribution strategy that we prove to be optimal. We empirically show that our method yields more robust predictions in terms of predictive performance under high PLL noise levels, handling out-of-distribution examples, and handling adversarial perturbations on the test instances.