LSVRC15">(Russakovsky et al. 2015)</span> dataset. At the heart of the algorithm, a <em>gradient ascent</em> (see <a href="#subsec:gradients" data-reference-type="ref" data-reference="subsec:gradients">3.2</a>) takes place between a <em>loss</em> and an input. Loss, in this context, refers to the activations of a layer given some input. The <em>gradient</em> is said to ascend because there is incremental manipulation on the input to a next iteration of the same routine. Thus, in time, the input is ‘steere