SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

Understand

Data mixing augmentation has proved effective in training deep models.

• Recent methods mix labels mainly based on the mixture proportion of image pixels.
• As the main discriminative information of a fine-grained image usually resides in subtle regions, methods along this line are prone to heavy label noise in fine-grained recognition.
• We propose in this paper a novel scheme, termed as Semantically Proportional Mixing (SnapMix), which exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data.