Mixup

2000년 도입 · 논문 651편에서 사용

Mixup is a data augmentation technique that generates a weighted combination of random image pairs from the training data. Given two images and their ground truth labels: $\left(x\_{i}, y\_{i}\right), \left(x\_{j}, y\_{j}\right)$, a synthetic training example $\left(\hat{x}, \hat{y}\right)$ is generated as: $$ \hat{x} = \lambda{x\_{i}} + \left(1 − \lambda\right){x\_{j}} $$ $$ \hat{y} = \lambda{y\_{i}} + \left(1 − \lambda\right){y\_{j}} $$ where $\lambda \sim \text{Beta}\left(\alpha = 0.2\right)$ is independently sampled for each augmented example.

출처: mixup: Beyond Empirical Risk Minimization

소개 논문: mixup: Beyond Empirical Risk Minimization