Achieving Unbiased Multi-Instance Learning via Balanced Fine-Grained Positive-Unlabeled Learning

Abstract: In real-world applications, it is often challenging to detect anomalous samples when the anomalous information they contain is extremely limited. In such cases, both macro-level and micro-level detection using multi-instance learning (MIL) encounter significant difficulties. The former struggles because normal and anomalous samples are highly similar and hard to distinguish at the macro level, while the latter is limited by the lack of labels at the micro level. In MIL, micro-level labels are inferred from macro-level labels, which can lead to severe bias. Moreover, the more imbalanced the distribution between normal and anomalous samples, the more pronounced these limitations become. In this study, we observe that the MIL problem can be elegantly transformed into a fine-grained Positive-Unlabeled (PU) learning problem. This transformation allows us to address the imbalance issue in an unbiased manner using a micro-level balancing mechanism. To this end, we propose a novel framework-Balanced Fine-Grained Positive-Unlabeled (BFGPU)-based on rigorous theoretical foundations to address the challenges above. Extensive experiments on both public and real-world datasets demonstrate the effectiveness of BFGPU, which outperforms existing methods, even in extreme scenarios where both macro and micro-level distributions are highly imbalanced. The code is open-sourced at https://github.com/BFGPU/BFGPU.