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We design a new algorithm for batch active learning with deep neural networkmodels. Our algorithm, Batch Active learning by Diverse Gradient Embeddings(BADGE), samples groups of points that are disparate and high-magnitude whenrepresented in a hallucinated gradient space, a strategy designed toincorporate both predictive uncertainty and sample diversity into everyselected batch. Crucially, BADGE trades off between diversity and uncertaintywithout requiring any hand-tuned hyperparameters. We show that while otherapproaches sometimes succeed for particular batch sizes or architectures, BADGEconsistently performs as well or better, making it a versatile option forpractical active learning problems.

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds