darts
The Differentiable Architecture Search method leverages continuous relaxation and gradient descent for efficient design of convolutional and recurrent architectures applicable in image classification and language modeling. Compatible with a single GPU setup, it targets datasets like CIFAR-10, ImageNet, PTB, and WikiText-2. Pretrained models facilitate swift evaluations, while 2nd-order approximations aid in searching for optimal architectures based on validation outcomes. Comprehensive training with full-sized models verifies their performance. Visualization tools further enhance understanding of architectural design, all within Python and PyTorch environments, providing a notable advancement for machine learning architecture optimization.
