  We propose Joint-DetNAS, a unified NAS framework for object detection, which
integrates 3 key components: Neural Architecture Search, pruning, and Knowledge
Distillation. Instead of naively pipelining these techniques, our Joint-DetNAS
optimizes them jointly. The algorithm consists of two core processes: student
morphism optimizes the student's architecture and removes the redundant
parameters, while dynamic distillation aims to find the optimal matching
teacher. For student morphism, weight inheritance strategy is adopted, allowing
the student to flexibly update its architecture while fully utilize the
predecessor's weights, which considerably accelerates the search; To facilitate
dynamic distillation, an elastic teacher pool is trained via integrated
progressive shrinking strategy, from which teacher detectors can be sampled
without additional cost in subsequent searches. Given a base detector as the
input, our algorithm directly outputs the derived student detector with high
performance without additional training. Experiments demonstrate that our
Joint-DetNAS outperforms the naive pipelining approach by a great margin. Given
a classic R101-FPN as the base detector, Joint-DetNAS is able to boost its mAP
from 41.4 to 43.9 on MS COCO and reduce the latency by 47%, which is on par
with the SOTA EfficientDet while requiring less search cost. We hope our
proposed method can provide the community with a new way of jointly optimizing
NAS, KD and pruning.
