{"id":617298,"date":"2019-06-09T09:00:37","date_gmt":"2019-06-09T16:00:37","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=617298"},"modified":"2020-08-28T16:49:10","modified_gmt":"2020-08-28T23:49:10","slug":"deep-batch-active-learning-by-diverse-uncertain-gradient-lower-bounds","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/deep-batch-active-learning-by-diverse-uncertain-gradient-lower-bounds\/","title":{"rendered":"Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds"},"content":{"rendered":"

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 hand-tuned 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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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. 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