As others have noted, this is data augmentation, and it's incredibly useful to increase variation in training data to help decrease overfitting. It's not a sil

As others have noted, this is data augmentation, and it's incredibly useful to increase variation in training data to help decrease overfitting. It's not a silver bullet. It won't capture the natural variations that happen in the real world. But new forms of augmentation (like OP) are helping us get closer. For example, MixMatch creates "mosaic" images by combining images across the training set [1]. In object detection, bounding box only augmentations are improving models by introducing variation [2]. And an anecdote: I work on https://roboflow.ai https://roboflow.ai , and we've seen customers make production-ready results from datasets <20 images based on techniques like these. [1] https://arxiv.org/abs/1905.02249 https://arxiv.org/abs/1905.02249 [2] https://arxiv.org/pdf/1906.11172.pdf https://arxiv.org/pdf/1906.11172.pdf