By looking at those images, the researchers had a better idea of what the neural network was up to at that instant. One team in particular, from the Visual Geometry Group at the University of Oxford, had taken an interesting approach to analyzing how successful vision systems can recognize (classify) objects: at a certain point in the training process, they got the networks to generate images of what they were perceiving. ConvNets are a specialized form generally used for vision recognition they take the biological metaphor farther by not only using a neuron-style learning system, but by employing the neurons in a similar fashion to the way light receptors are arranged in the visual cortex. His curiosity was piqued by one the abiding mysteries of neural nets and deep learning: why did they work so well and what the hell went on inside them? Others had been asking the same question, using what are known as convolutional neural nets (ConvNets) to probe vision recognition systems at various points in the process. As an NN newbie, Mordvintsev was teaching himself about the field, absorbing key papers and playing with systems already trained to recognize certain objects.
