41,793 research outputs found
TOFA: Transfer-Once-for-All
Weight-sharing neural architecture search aims to optimize a configurable
neural network model (supernet) for a variety of deployment scenarios across
many devices with different resource constraints. Existing approaches use
evolutionary search to extract a number of models from a supernet trained on a
very large data set, and then fine-tune the extracted models on the typically
small, real-world data set of interest. The computational cost of training thus
grows linearly with the number of different model deployment scenarios. Hence,
we propose Transfer-Once-For-All (TOFA) for supernet-style training on small
data sets with constant computational training cost over any number of edge
deployment scenarios. Given a task, TOFA obtains custom neural networks, both
the topology and the weights, optimized for any number of edge deployment
scenarios. To overcome the challenges arising from small data, TOFA utilizes a
unified semi-supervised training loss to simultaneously train all subnets
within the supernet, coupled with on-the-fly architecture selection at
deployment time
SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation
Accurate medical image segmentation especially for echocardiographic images
with unmissable noise requires elaborate network design. Compared with manual
design, Neural Architecture Search (NAS) realizes better segmentation results
due to larger search space and automatic optimization, but most of the existing
methods are weak in layer-wise feature aggregation and adopt a ``strong
encoder, weak decoder" structure, insufficient to handle global relationships
and local details. To resolve these issues, we propose a novel semi-supervised
hybrid NAS network for accurate medical image segmentation termed SSHNN. In
SSHNN, we creatively use convolution operation in layer-wise feature fusion
instead of normalized scalars to avoid losing details, making NAS a stronger
encoder. Moreover, Transformers are introduced for the compensation of global
context and U-shaped decoder is designed to efficiently connect global context
with local features. Specifically, we implement a semi-supervised algorithm
Mean-Teacher to overcome the limited volume problem of labeled medical image
dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate
that SSHNN outperforms state-of-the-art approaches and realizes accurate
segmentation. Code will be made publicly available.Comment: Submitted to ICASSP202
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