In deep learning 1x1 and 3x3 convolutions are used for different purposes. 3x3 corresponds to a convenient convolution, that applies some filters to the input data. Whereas 1x1 is something like a Network in Network. Conceptually it is close to a MLP (with no hidden layer) applied to the channel values of every pixel.