Using Early-Learning Regularization to Classify Real-World Noisy Data

Understand

The memorization problem is well-known in the field of computer vision.

• Liu et al.
• propose a technique called Early-Learning Regularization, which improves accuracy on the CIFAR datasets when label noise is present.
• This project replicates their experiments and investigates the performance on a real-world dataset with intrinsic noise.