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Found 5 papers, 4 papers with code
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS.
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NAS
Neural Architecture Search (NAS) aims to automatically find optimal neural network architectures in an efficient way.
AGNAS: Attention-Guided Micro- and Macro-Architecture Search
We utilize the attention weights to represent the importance of the relevant operations for the micro search or the importance of the relevant blocks for the macro search.
ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap.
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search
Differentiable Neural Architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency.
Ranked #9 on
Neural Architecture Search
on CIFAR-100
