# GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation
## 4 Experiments and Results
### Classification  
We evaluated the performance of GaussianMix data augmentation on both the CIFAR-100 and ImageNet datasets using the following standard deep residual networks: ResNet-18, ResNet-50, and WideResNet-28-10 for CIFAR-100 along with ResNet-50 and ResNet-101 for ImageNet. For CIFAR-100, we trained the models for 200 epochs with a batch size of 128 using Stochastic Gradient Descent (SGD), Nesterov momentum of 0.9, and weight decay of \(5\times 10^{-4}\). The learning rate was initially set to 0.1 and then decreased by a factor of 0.2 at 60, 120, and 160 epochs. We set the \(\beta\) hyperparameter to 1.0 and the \(\sigma\) hyperparameter to 10.0, as described in Section 5.3. We conducted the experiment three times and report the mean Top-1 error along with the corresponding standard deviation. For ImageNet, we trained the models for 300 epochs with a batch size of 128, using a similar learning rate schedule (initially set to 0.1, then decaying by 0.1 at epochs 75, 150, and 225). The \(\sigma\) hyperparameter was set to 40.0, as detailed in Section 5.3. As shown in Table 1, on CIFAR-100, GaussianMix outperforms all other augmentation methods when applied to the ResNet-18 and ResNet-50 architectures, achieving new best top-1 error scores of 18.97% and 18.50%, respectively. (The top-1 error is computed as the percentage of test samples for which the model's highest-probability prediction does not match the ground truth label, and is equivalent to \(100\ -\ top\text{-}1\ accuracy(\%)\).). For WideResNet, GaussianMix outperforms other state-of-the-art methods such as Cutout [12], CutMix [13], and SaliencyMix [15] while delivering comparable performance to PuzzleMix [14]. Notably, PuzzleMix requires solving a dual-optimization problem that introduces substantial computational overhead and needs 1200 epochs to converge, whereas GaussianMix converges in just 200 epochs. Similarly, on ImageNet, our method outperforms all state-of-the-art methods while achieving compa-table performance to PuzzleMix for both ResNet-50 and ResNet-101 as shown in Table 2. For ResNet-50, GaussianMix provides a significant improvement in top-1 error of 1.69%, 1.34%, and 0.16% over Cutout [12], Mixup [26], and CutMix [13], respectively. Furthermore, it establishes a new benchmark by achieving a top-1 error of 19.80% on ImageNet when applied to ResNet-101. In addition, the proposed method outperforms SaliencyMix [15] while significantly reducing computational complexity thanks to GaussianMix eliminating the need for saliency detection. The results across both datasets highlight the efficacy of the GaussianMix augmentation strategy, emphasizing its ability to enhance model performance while reducing computational complexity compared to existing state-of-the-art methods. \begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Top-1 Error** \\ \hline ResNet-18 (Baseline) & 22.46 \(\pm\) 0.3 \\ ResNet-18+CutOut & 21.96 \(\pm\) 0.24 \\ ResNet-18+FMix & 20.15 \(\pm\) 0.27 \\ ResNet-18+CutMix & 19.42 \(\pm\) 0.24 \\ ResNet-18+SaliencyMix & 19.29 \(\pm\) 0.21 \\ ResNet-18+PuzzleMix (200 Epoch) & 31.78 \(\pm\) 0.20 \\ ResNet-18+PuzzleMix (1200 Epoch) & 19.66 \(\pm\) 0.18 \\ ResNet-18+GaussianMix & **18.97 \(\pm\) 0.14** \\ \hline ResNet-50 (BASELINE) & 21.58 \(\pm\) 0.43 \\ ResNet-50+CutOut & 21.38 \(\pm\) 0.69 \\ ResNet-50+CutMix & 18.72 \(\pm\) 0.23 \\ ResNet-50+SaliencyMix & 18.57 \(\pm\) 0.29 \\ ResNet-50+PuzzleMix (200 Epoch) & 26.61 \(\pm\) 0.51 \\ ResNet-50+PuzzleMix (1200 Epoch) & 17.17 \(\pm\) 0.42 \\ ResNet-50+GaussianMix & **18.50 \(\pm\) 0.37** \\ \hline WideResNet28-10 (BASELINE) & 18.80 \(\pm\) 0.08 \\ WideResNet28-10+CutOut & 18.41 \(\pm\) 0.27 \\ WideResNet28-10+FMix & 17.97 \(\pm\) 0.27 \\ WideResNet28-10+CutMix & 16.66 \(\pm\) 0.20 \\ WideResNet28-10+SaliencyMix & 16.56 \(\pm\) 0.17 \\ WideResNet28-10+PuzzleMix & **16.23 \(\pm\) 0.17** \\ WideResNet28-10+GaussianMix & 16.28 \(\pm\) 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification comparison of various data augmentation methods on CIFAR-100.