2021 International Joint Conference on Neural Networks (IJCNN) | 2021
Deep Active Learning with Relative Label Feedback: An Application to Facial Age Estimation
Abstract
Deep learning has emerged as an effective machine learning algorithm to automatically learn a representative set of features and has revolutionized multimedia computing research. However, training a reliable deep model necessitates a large amount of labeled training data, which is time-consuming and labor-intensive to acquire. Active Learning (AL) algorithms address this challenge by automatically identifying the salient and exemplar samples from large amounts of unlabeled data; this drastically reduces human annotation effort, as only a handful of samples, that are identified by the algorithm, need to be labeled manually. However, in applications like vision-based facial age estimation, providing the exact labels (age of a person) may be challenging even for human annotators, as it maybe difficult to accurately estimate the age of a person merely from a facial image; it maybe much easier to provide relative label feedback, such as whether a particular subject is older than another subject. In this paper, we propose a novel deep active learning algorithm (DALRel) which requires only relative label feedback in response to the queried samples. We formulate a loss function relevant to the research task and exploit the gradient descent algorithm to optimize the loss and train the deep network. To the best of our knowledge, this is the first research effort to develop an active learning framework to train a deep neural network, which poses only relative label queries to the labeling oracles. Our extensive empirical studies demonstrate the promise and potential of this method for real-world active learning applications, where providing the exact labels to queried instances can be challenging.