Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
This addresses the computational bottleneck for researchers and practitioners in active learning, though it is an incremental improvement on existing methods.
The paper tackles the computational infeasibility of look-ahead active learning strategies by using neural tangent kernels to approximate retraining, enabling sequential updates without full retraining. Their method outperforms other look-ahead strategies by large margins and matches or beats state-of-the-art methods on benchmark datasets.
We propose a new method for approximating active learning acquisition strategies that are based on retraining with hypothetically-labeled candidate data points. Although this is usually infeasible with deep networks, we use the neural tangent kernel to approximate the result of retraining, and prove that this approximation works asymptotically even in an active learning setup -- approximating "look-ahead" selection criteria with far less computation required. This also enables us to conduct sequential active learning, i.e. updating the model in a streaming regime, without needing to retrain the model with SGD after adding each new data point. Moreover, our querying strategy, which better understands how the model's predictions will change by adding new data points in comparison to the standard ("myopic") criteria, beats other look-ahead strategies by large margins, and achieves equal or better performance compared to state-of-the-art methods on several benchmark datasets in pool-based active learning.