  Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling'
(MP) layers to extract deformation-invariant features, but we argue in favor of
a more refined treatment. First, we introduce epitomic convolution as a
building block alternative to the common convolution-MP cascade of DCNNs; while
having identical complexity to MP, Epitomic Convolution allows for parameter
sharing across different filters, resulting in faster convergence and better
generalization. Second, we introduce a Multiple Instance Learning approach to
explicitly accommodate global translation and scaling when training a DCNN
exclusively with class labels. For this we rely on a `patchwork' data structure
that efficiently lays out all image scales and positions as candidates to a
DCNN. Factoring global and local deformations allows a DCNN to `focus its
resources' on the treatment of non-rigid deformations and yields a substantial
classification accuracy improvement. Third, further pursuing this idea, we
develop an efficient DCNN sliding window object detector that employs explicit
search over position, scale, and aspect ratio. We provide competitive image
classification and localization results on the ImageNet dataset and object
detection results on the Pascal VOC 2007 benchmark.
