  We propose a novel information bottleneck (IB) method named Drop-Bottleneck,
which discretely drops features that are irrelevant to the target variable.
Drop-Bottleneck not only enjoys a simple and tractable compression objective
but also additionally provides a deterministic compressed representation of the
input variable, which is useful for inference tasks that require consistent
representation. Moreover, it can jointly learn a feature extractor and select
features considering each feature dimension's relevance to the target task,
which is unattainable by most neural network-based IB methods. We propose an
exploration method based on Drop-Bottleneck for reinforcement learning tasks.
In a multitude of noisy and reward sparse maze navigation tasks in VizDoom
(Kempka et al., 2016) and DMLab (Beattie et al., 2016), our exploration method
achieves state-of-the-art performance. As a new IB framework, we demonstrate
that Drop-Bottleneck outperforms Variational Information Bottleneck (VIB)
(Alemi et al., 2017) in multiple aspects including adversarial robustness and
dimensionality reduction.
