We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any handtuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.
Finally BADGE REF samples points which are high magnitude and diverse in a hallucinated gradient space with respect to the last layer of a neural network.
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
2020 International Conference on Learning Representations
Computer Science Mathematics