Neural Architecture Search (NAS) is a method used by researchers to automate the development of optimal neural network architectures. These architectures are created for a specific task and are then evaluated against a performance metric on a validation dataset. However, earlier NAS methods encountered several issues due to the need to extensively train each candidate architecture. The process was computationally expensive and time-consuming. Various techniques were proposed to speed up NAS, but computational complexity remained a challenge.
A new method, NASGraph, has been introduced in a research paper that significantly reduces the computational load of NAS. Instead of training every candidate architecture, NASGraph converts them into graph representations and uses graph metrics to efficiently approximate their performance. This is achieved by initially breaking down the neural network into graph blocks that include layers like convolutions and activations. For every block, the method calculates how much each input channel contributes to the output channels by executing a single forward pass.
After this, the architecture is incorporated into a graph. NASGraph then calculates the average number of connections per node to rank the quality of the architecture. However, the researchers present surrogate models that have fewer computational requirements, which doubles the acceleration of the process. Major speed-ups are then achieved by trading off accuracy for these reduced computational settings.
NASGraph was evaluated by being tested on several NAS milestones. The researchers discovered that the average degree metric strongly correlated with the actual performance of the architecture, exceeding the performance of earlier training-free NAS methods. Furthermore, combining this graph measure with other training-free metrics resulted in even higher ranking capabilities, creating state-of-the-art Spearman ranking correlations that exceeded 0.8 on databases such as CIFAR-10, CIFAR-100, and ImageNet-16-120.
The new NASGraph system represents a paradigm shift in neural architecture search by harnessing a groundbreaking, graph-based methodology. This system eliminates the computational bottleneck found in previous NAS methods by removing the need for architecture training. With its exceptional performance, low bias, data-agnostic nature, and notable efficiency, NASGraph has the potential to transform the future of neural architecture exploration, leading to the discovery of powerful AI models across various applications.
Authors credit the developers of the research project for their findings. In addition to following them on twitter and subscribing to their various channels, readers are encouraged to delve into the research paper itself. With a subscriber base of over 41,000, these new advancements in NAS methodologies are sure to spark widespread interest and discussion amongst the machine learning community.