This document discusses active learning techniques called Deep Badge Active Learning. It proposes using gradient embeddings to represent samples and k-means++ initialization for sample selection. Specifically, it uses the gradient embedding for feature representation, then performs k-means++ initialization to select samples by finding those with the maximum 2-norm and those farthest from existing samples, adding them to the set iteratively. This aims to select a diverse set of samples, similar to how binary search works. The technique could improve over entropy-based and core-set selection approaches for active learning with convolutional neural networks.