Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification

• URL: http://arxiv.org/abs/2009.14448v1
• Date: Wed, 30 Sep 2020 05:19:56 GMT
• Title: Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification
• Authors: Bindya Venkatesh and Jayaraman J. Thiagarajan
• Abstract summary: Deep predictive models rely on human supervision in the form of labeled training data. We propose Ask-n-Learn, an active learning approach based on gradient embeddings obtained using the pesudo-labels estimated in each of the algorithm.
• Score: 29.43017692274488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep predictive models rely on human supervision in the form of labeled training data. Obtaining large amounts of annotated training data can be expensive and time consuming, and this becomes a critical bottleneck while building such models in practice. In such scenarios, active learning (AL) strategies are used to achieve faster convergence in terms of labeling efforts. Existing active learning employ a variety of heuristics based on uncertainty and diversity to select query samples. Despite their wide-spread use, in practice, their performance is limited by a number of factors including non-calibrated uncertainties, insufficient trade-off between data exploration and exploitation, presence of confirmation bias etc. In order to address these challenges, we propose Ask-n-Learn, an active learning approach based on gradient embeddings obtained using the pesudo-labels estimated in each iteration of the algorithm. More importantly, we advocate the use of prediction calibration to obtain reliable gradient embeddings, and propose a data augmentation strategy to alleviate the effects of confirmation bias during pseudo-labeling. Through empirical studies on benchmark image classification tasks (CIFAR-10, SVHN, Fashion-MNIST, MNIST), we demonstrate significant improvements over state-of-the-art baselines, including the recently proposed BADGE algorithm.

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