  We develop methods for detector learning which exploit joint training over
both weak and strong labels and which transfer learned perceptual
representations from strongly-labeled auxiliary tasks. Previous methods for
weak-label learning often learn detector models independently using latent
variable optimization, but fail to share deep representation knowledge across
classes and usually require strong initialization. Other previous methods
transfer deep representations from domains with strong labels to those with
only weak labels, but do not optimize over individual latent boxes, and thus
may miss specific salient structures for a particular category. We propose a
model that subsumes these previous approaches, and simultaneously trains a
representation and detectors for categories with either weak or strong labels
present. We provide a novel formulation of a joint multiple instance learning
method that includes examples from classification-style data when available,
and also performs domain transfer learning to improve the underlying detector
representation. Our model outperforms known methods on ImageNet-200 detection
with weak labels.
