As we know that to improve the performance of an ML model, we need to do some data preprocessing steps before actually training the model. One of the data preprocessing steps is Data Augmentation. Over recent years data augmentation has resulted in highly improving the model’s performance. As data augmentation techniques tend to increase the performance of models, efforts were also made to improve them. One such technique introduced recently is **CutMix **which we are gonna discuss in this article.
Note this article reflects the study of the original paper that introduced CutMix augmentation and thus some definitions and phrases are taken from it.
Table of Contents —
- Need for CutMix
- Cutmix and other augmentations
- Algorithm
- Visualizing CAMs
- Models’ Performance
- Conclusion
- References
Need for CutMix
Before CutMix was introduced, **Regional Dropout **strategies were used as a data augmentation step to enhance the performance of the CNNs. These augmentations remove informative pixels in training images by overlaying them with a patch of either black pixels or random noise. This causes information loss and hence less efficiency of the models. However, it also makes the model focus on non-discriminative parts of the object. Hence a strategy was to be introduced which had the effect of regional dropout but should also be able to retain the regularization effect and so CutMix was introduced. An example of a regional dropout strategy could be —
CutMix and other augmentations
Let’s have a short description of CutMix and some other augmentation techniques.
CutMix
In CutMix augmentation we cut and paste random patches between the training images. The ground truth labels are mixed in proportion to the area of patches in the images. CutMix increases localization ability by making the model to focus on less discriminative parts of the object being classified and hence is also well suited for tasks like object detection.
Mixup
In Mixup augmentation two samples are mixed together by linear interpolation of their images and labels. _Mixup _samples suffer from unrealistic output and ambiguity among the labels and hence cannot perform well on tasks like image localization and object detection.
Cutout
Cutout augmentation is a kind of regional dropout strategy in which a random patch from an image is zeroed out (replaced with black pixels). Cutout samples suffer from the decrease in information and regularization capability.
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