The Workshop on Neural Architecture Search for Computer Vision in the Wild (NASFW) will be held in conjunction with the WACV 2020 conference.
Over the past few years, deep learning (DL) has helped advance computer vision by leaps and bounds. From achieving super-human accuracy in image classification tasks to dramatically improving image generation, deep learning based algorithms have dominated the performance charts and established state-of-the-art time after time. Although deep learning has helped in minimizing the reliance on domain specific hand-crafted features, it is in fact the hundreds of hours of human machine learning expertise that goes into squeezing the last bit of performance from these models. From doing data processing to taking several decisions on choosing the right model architecture and associated hyperparameters, deep learning is still heavily dependent on humans to achieve desired results. This dependence limits the application of deep learning in several domains especially the non-technical ones such as health, education and retail, where human expertise in deep learning may not be readily available. Automated Machine Learning, which incorporates methods such as neural architecture search (NAS) and hyperparameter optimization (HPO), provides approaches/systems to help deep learning be used for various applications without any expert knowledge of deep learning. It can help democratize deep learning by reducing the need for DL expertise in application development.