  The automation of neural architecture design has been a coveted alternative
to human experts. Recent works have small search space, which is easier to
optimize but has a limited upper bound of the optimal solution. Extra human
design is needed for those methods to propose a more suitable space with
respect to the specific task and algorithm capacity. To further enhance the
degree of automation for neural architecture search, we present a Neural
Search-space Evolution (NSE) scheme that iteratively amplifies the results from
the previous effort by maintaining an optimized search space subset. This
design minimizes the necessity of a well-designed search space. We further
extend the flexibility of obtainable architectures by introducing a learnable
multi-branch setting. By employing the proposed method, a consistent
performance gain is achieved during a progressive search over upcoming search
spaces. We achieve 77.3% top-1 retrain accuracy on ImageNet with 333M FLOPs,
which yielded a state-of-the-art performance among previous auto-generated
architectures that do not involve knowledge distillation or weight pruning.
When the latency constraint is adopted, our result also performs better than
the previous best-performing mobile models with a 77.9% Top-1 retrain accuracy.
