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We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
Here we aim to learn a better architecture of feature pyramid network for object detection.
#12 best model for Real-Time Object Detection on COCO (MAP metric)
In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.
#98 best model for Image Classification on ImageNet
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention.
Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.
Our cell achieves a test set perplexity of 62. 4 on the Penn Treebank, which is 3. 6 perplexity better than the previous state-of-the-art model.
#5 best model for Neural Architecture Search on CIFAR-10 Image Classification
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device?
#113 best model for Image Classification on ImageNet
We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set.
#2 best model for Neural Architecture Search on CIFAR-10 Image Classification
We address the challenging problem of efficient deep learning model deployment across many devices and diverse constraints, from general-purpose hardware to specialized accelerators.