1 research outputs found
Deep Active Learning by Model Interpretability
Recent successes of Deep Neural Networks (DNNs) in a variety of research
tasks, however, heavily rely on the large amounts of labeled samples. This may
require considerable annotation cost in real-world applications. Fortunately,
active learning is a promising methodology to train high-performing model with
minimal annotation cost. In the deep learning context, the critical question of
active learning is how to precisely identify the informativeness of samples for
DNN. In this paper, inspired by piece-wise linear interpretability in DNN, we
introduce the linearly separable regions of samples to the problem of active
learning, and propose a novel Deep Active learning approach by Model
Interpretability (DAMI). To keep the maximal representativeness of the entire
unlabeled data, DAMI tries to select and label samples on different linearly
separable regions introduced by the piece-wise linear interpretability in DNN.
We focus on modeling Multi-Layer Perception (MLP) for modeling tabular data.
Specifically, we use the local piece-wise interpretation in MLP as the
representation of each sample, and directly run K-Center clustering to select
and label samples. To be noted, this whole process of DAMI does not require any
hyper-parameters to tune manually. To verify the effectiveness of our approach,
extensive experiments have been conducted on several tabular datasets. The
experimental results demonstrate that DAMI constantly outperforms several
state-of-the-art compared approaches