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Status: Unchecked ×Keyword: efficient neural network architecture ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Evolving Search Space for Neural Architecture Search

   Ci, Lin, Sun, Chen, Zhang and Ouyang · IEEE/CVF International Conference on Computer Vision (ICCV) · 2021

   The paper presents Neural Search-space Evolution, a method that iteratively refines a neural architecture search space, and reports 77.3% top-1 ImageNet accuracy at 333M FLOPs.

   Unchecked2 claims
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   1. UncheckedWith a latency constraint, the authors' searched network reaches 77.9% Top-1 retrain accuracy, which they say beats the previous best mobile models.“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.”
   2. Unchecked“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.”