  Neural Architecture Search (NAS) has gained attraction due to superior
classification performance. Differential Architecture Search (DARTS) is a
computationally light method. To limit computational resources DARTS makes
numerous approximations. These approximations result in inferior performance.
We propose to fine-tune DARTS using fixed operations as they are independent of
these approximations. Our method offers a good trade-off between the number of
parameters and classification accuracy. Our approach improves the top-1
accuracy on Fashion-MNIST, CompCars, and MIO-TCD datasets by 0.56%, 0.50%, and
0.39%, respectively compared to the state-of-the-art approaches. Our approach
performs better than DARTS, improving the accuracy by 0.28%, 1.64%, 0.34%,
4.5%, and 3.27% compared to DARTS, on CIFAR-10, CIFAR-100, Fashion-MNIST,
CompCars, and MIO-TCD datasets, respectively.
