Increasing Data Diversity with Iterative Sampling to Improve Performance

11/05/2021
by   Devrim Cavusoglu, et al.
0

As a part of the Data-Centric AI Competition, we propose a data-centric approach to improve the diversity of the training samples by iterative sampling. The method itself relies strongly on the fidelity of augmented samples and the diversity of the augmentation methods. Moreover, we improve the performance further by introducing more samples for the difficult classes especially providing closer samples to edge cases potentially those the model at hand misclassifies.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset