[Componica]

You're probably right in terms of the NN research world, but I've been staring at a wall reminiscing for a 1/2 hour and concluded... Neural networks weren’t widely used in the late 90s and early 00s in the field of computer vision.

Face detection was dominated by Viola-Jones and Haar features, facial feature detection relied on active shape and active appearance models (AAMs), with those iconic Delaunay triangles becoming the emblem of facial recognition. SVMs were used to highlight tumors, while kNNs and hand-tuned feature detectors handled tumors and lesions. Dynamic programming was used to outline CTs and MRIs of hearts, airways, and other structures, Hough transforms were used for pupil tracking, HOG features were popular for face, car, and body detectors, and Gaussian models & Hidden Markov Models were standard in speech recognition. I remember seeing a few papers attempting to stick a 3-layer NN on the outputs of AAMs with limited success.

The Yann LeCun paper felt like a breakthrough to me. It seemed biologically plausible, given what I knew of the Neocognitron and the visual cortex, and the shared weights of the kernels provided a way to build deep models beyond one or two hidden layers.

At the time, I felt like Cassandra, going from past colleagues and computer vision-based companies in the region, trying to convey to them just how much of a game changer that paper was.