Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search

by   Zixiang Ding, et al.

Different from other deep scalable architecture based NAS approaches, Broad Neural Architecture Search (BNAS) proposes a broad one which consists of convolution and enhancement blocks, dubbed Broad Convolutional Neural Network (BCNN) as search space for amazing efficiency improvement. BCNN reuses the topologies of cells in convolution block, so that BNAS can employ few cells for efficient search. Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology. However, BNAS suffers some drawbacks: 1) insufficient representation diversity for feature fusion and enhancement, and 2) time consuming of knowledge embedding design by human expert. In this paper, we propose Stacked BNAS whose search space is a developed broad scalable architecture named Stacked BCNN, with better performance than BNAS. On the one hand, Stacked BCNN treats mini-BCNN as the basic block to preserve comprehensive representation and deliver powerful feature extraction ability. On the other hand, we propose Knowledge Embedding Search (KES) to learn appropriate knowledge embeddings. Experimental results show that 1) Stacked BNAS obtains better performance than BNAS, 2) KES contributes to reduce the parameters of learned architecture with satisfactory performance, and 3) Stacked BNAS delivers state-of-the-art efficiency of 0.02 GPU days.


page 1

page 2

page 3

page 4


Efficient Neural Architecture Search: A Broad Version

Efficient Neural Architecture Search (ENAS) achieves novel efficiency fo...

Neural Architecture Construction using EnvelopeNets

In recent years, advances in the design of convolutional neural networks...

Faster Gradient-based NAS Pipeline Combining Broad Scalable Architecture with Confident Learning Rate

In order to further improve the search efficiency of Neural Architecture...

Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach

Efficient identification of people and objects, segmentation of regions ...

Architecture representations for quantum convolutional neural networks

The Quantum Convolutional Neural Network (QCNN) is a quantum circuit mod...

Customizable Architecture Search for Semantic Segmentation

In this paper, we propose a Customizable Architecture Search (CAS) appro...

EfficientTDNN: Efficient Architecture Search for Speaker Recognition in the Wild

Speaker recognition refers to audio biometrics that utilizes acoustic ch...

Please sign up or login with your details

Forgot password? Click here to reset