392 papers with code • 19 benchmarks • 25 datasets
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.
Ranked #4 on Image Classification on iNaturalist
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Ranked #151 on Image Classification on ImageNet
In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture".
Ranked #23 on Image Classification on ImageNet ReaL
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.
Ranked #1 on Action Recognition on EPIC-KITCHENS-100
By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.
Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space.
Ranked #7 on Semantic Segmentation on PASCAL VOC 2012 val
We present MorphNet, an approach to automate the design of neural network structures.
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.
We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search.