NAS without training (WOT) score is such a metric, which estimates the final trained accuracy of the architecture through the ability to distinguish different inputs in the activation layer.
To solve this issue, we propose a Cross-Domain Predictor (CDP), which is trained based on the existing NAS benchmark datasets (e. g., NAS-Bench-101), but can be used to find high-performance architectures in large-scale search spaces.
Performance predictors can greatly alleviate the prohibitive cost of NAS by directly predicting the performance of DNNs.
As a consequence, it requires the designers to develop expertise in both CF and DNNs, which limits the application of deep learning methods in CF and the accuracy of recommended results.
The paper conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform.
Specifically, a homogeneous architecture augmentation algorithm is proposed in HAAP to generate sufficient training data taking the use of homogeneous representation.
Transgender community is experiencing a huge disparity in mental health conditions compared with the general population.
Deep Neural Networks (DNNs) have achieved great success in many applications.
Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classification tasks.