Exploiting Counter-Examples for Active Learning with Partial labels
Fei Zhang Yunjie Ye Lei Feng Zhongwen Rao Jieming Zhu Marcus Kalander Chen Gong Jianye Hao Bo Han
This paper studies a new problem, \emph{active learning with partial labels}
(ALPL). In this setting, an oracle annotates the query samples with partial
labels, relaxing the oracle from the demanding accurate labeling process. To
address ALPL, we first build an intuitive baseline that can be seamlessly
incorporated into existing AL frameworks. Though effective, this baseline is
still susceptible to the \emph{overfitting}, and falls short of the
representative partial-label-based samples during the query process. Drawing
inspiration from human inference in cognitive science, where accurate
inferences can be explicitly derived from \emph{counter-examples} (CEs), our
objective is to leverage this human-like learning pattern to tackle the
\emph{overfitting} while enhancing the process of selecting representative
samples in ALPL. Specifically, we construct CEs by reversing the partial labels
for each instance, and then we propose a simple but effective WorseNet to
directly learn from this complementary pattern. By leveraging the distribution
gap between WorseNet and the predictor, this adversarial evaluation manner
could enhance both the performance of the predictor itself and the sample
selection process, allowing the predictor to capture more accurate patterns in
the data. Experimental results on five real-world datasets and four benchmark
datasets show that our proposed method achieves comprehensive improvements over
ten representative AL frameworks, highlighting the superiority of WorseNet. The
source code will be available at \url{https://github.com/Ferenas/APLL}.