  We present a novel method, SALAD, for the challenging vision task of adapting
a pre-trained "source" domain network to a "target" domain, with a small budget
for annotation in the "target" domain and a shift in the label space. Further,
the task assumes that the source data is not available for adaptation, due to
privacy concerns or otherwise. We postulate that such systems need to jointly
optimize the dual task of (i) selecting fixed number of samples from the target
domain for annotation and (ii) transfer of knowledge from the pre-trained
network to the target domain. To do this, SALAD consists of a novel Guided
Attention Transfer Network (GATN) and an active learning function, HAL. The
GATN enables feature distillation from pre-trained network to the target
network, complemented with the target samples mined by HAL using
transfer-ability and uncertainty criteria. SALAD has three key benefits: (i) it
is task-agnostic, and can be applied across various visual tasks such as
classification, segmentation and detection; (ii) it can handle shifts in output
label space from the pre-trained source network to the target domain; (iii) it
does not require access to source data for adaptation. We conduct extensive
experiments across 3 visual tasks, viz. digits classification (MNIST, SVHN,
VISDA), synthetic (GTA5) to real (CityScapes) image segmentation, and document
layout detection (PubLayNet to DSSE). We show that our source-free approach,
SALAD, results in an improvement of 0.5%-31.3%(across datasets and tasks) over
prior adaptation methods that assume access to large amounts of annotated
source data for adaptation.
