# GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation
## 2 Related Work  
### Data Augmentation  
Due to their large model sizes, a significant amount of data is required to train deep learning models. However, collecting labeled data is a time-consuming and tedious job. To address this problem, various Data Augmentation (DA) methods have been proposed; these apply different transformations on existing data to create new samples with high variation. Traditional DA methods [21, 22, 24, 25] increase the number of samples or their variations by applying simple image transformations, introducing color distortions, injecting noise, Gaussian smoothing, etc. However, traditional DA methods fail to enhance the model's robustness against occlusion. Several recent studies have suggested mixing different image patches to enhance the generalization ability of CNNs [12, 13, 14, 15, 16]. For example, Cutout erases random image regions [12]; however, this can hurt the model's performance, as the missing pixels alter the training data distribution [13]. Instead, Mixup [26] proposes mixing two training samples and their labels to generate an augmented image. However, the augmented images are locally ambiguous. Considering the limitations of Cutout [12] and Mixup [26], CutMix [13] proposes cutting a patch from a sample and mixing it with another training image. Due to the success of CutMix [13], Mixed Sample-based Data Augmentation (MSDA) techniques have drawn much attention. Because MSDA combines multiple images to generate new samples, it occludes various parts of the object, preventing the network from focusing only on the specific parts that are most discriminative; in other words, it guides the model to consider the less discriminative parts in order to enhance the model's generalization ability. In MSDA, the augmented image along with the mixed labels confers a label smoothing effect on the network that prevents the model from being overconfident [27]. MSDA techniques can be classified into two categories, namely, random patch-based and salient patch-based. In random patch-based methods, the source patch is selected in a random fashion and then mixed with the target image. CutMix [13] replaces the removed region of an image with a randomly selected patch from another image (source image) and mixes their labels according to the proportion of mixed pixels. FMix [28] strives to improve upon CutMix by employing arbitrarily-shaped binary masks generated from low-frequency components in  
Figure 1: Heatmaps for the number of each pixel selected as a patch on the CIFAR-100 and ImageNet datasets. Top row: SaliencyMix [15]. Bottom row: Proposed method (GaussianMix). GaussianMix helps the model to select source patch positions from the entire image. On the other hand, the results for SaliencyMix [15] are highly skewed to the left side. Fourier space instead of rectangular patch mixing. Random Image Cropping and Patching (RICAP) [29] cuts and mixes patches from four different training images to create a new augmented image. While these methods are simple and efficient, they do not consider the information contained in the selected patch. This may generate irrelevant augmented data, leading the network to learn unexpected feature representations [15]. To solve this problem, several salient patch-based mixing methods have been introduced. Saliency-based augmentation techniques select the source patch in such a way that it retains relevant information from the source object. Saliency information emphasizes the significant regions of an image based on the natural attention mechanisms of the Human Visual System (HVS) [14; 15; 16; 30], and is commonly employed for patch selection [14; 15; 16]. PuzzleMix [14] maximizes the saliency information in the augmented image by solving a dual optimization problem, i.e., it mixes two images so that the augmented image contains as much salient information as possible from the two images. Similarly, Co-mixup [16] combines and matches the collection of salient areas by utilizing inter-arrangements among the mini-batch.