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Title: Spatial Information is Overrated for Image Classification

Paper content:
Intuitively, image classification should profit from using spatial information.
Recent work, however, suggests that this might be overrated in standard CNNs.
In this paper, we are pushing the envelope and aim to further investigate the reliance on and necessity of spatial information.
We propose and analyze three methods, namely Shuffle Conv, GAP+FC and 1x1 Conv, that destroy spatial information during both training and testing phases.
We extensively evaluate these methods on several object recognition datasets (CIFAR100, Small-ImageNet, ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152, MobileNet, SqueezeNet).
Interestingly, we consistently observe that spatial information can be completely deleted from a significant number of layers with no or only small performance drops.
Despite the fantastic performances of convolutional neural networks (CNNs) on computer vision tasks, their inner workings remain mostly obfuscated to us and analyzing them results often in surprising results.
Generally, the majority of modern CNNs for image classification learn spatial information across all the convolutional layers: every layer in AlexNet, VGG, Inception, and ResNet applies 3×3 or larger filters.
Such design choice is based on the assumption that spatial information remains important at every convolutional layer to consecutively increase the access to a larger spatial context.
This is based on the observations that single local features can be ambiguous and should be related to other features in the same scene to make accurate predictions Torralba et al. (2003) ; Hoiem et al. (2008) .
Recent work on restricting the receptive field of CNN architectures, scrambling the inputs or using wavelet feature networks resulting in networks with shallow depth (Oyallon et al., 2017) have all found it to be possible to acquire competitive performances on the respective classification tasks.
This raises doubts on whether common CNNs learn representations of global context as small local features appear to be sufficient for classification.
We add to the list of surprising findings surrounding the inner workings of CNNs and present a rigorous investigation on the necessity of spatial information in standard CNNs by avoiding learning spatial information at multiple layers.
To this end, we propose three methods i.e., shuffle conv, GAP+FC and 1x1Conv, to eliminate the spatial information.
Surprisingly, we find that the modified CNNs i.e., without the ability to access any spatial information at last layers, can still achieve competitive results on several object recognition datasets.
This indicates that the spatial information is overrated for standard CNNs and not necessary to reach competitive performances.
In our experiments, the last layers of standard CNNs can be simplified by substituting them with our proposed GAP+FC or 1x1Conv layers which ignore spatial information, leading to a smaller model with less parameters.
Moreover, our novel simplifications can be adapted to a wide range of CNN architectures and maintain state-of-the-art performance on various image classification datasets.
The detail of the shuffle conv.
Each feature map from the input tensor will be randomly and independently shuffled before being fed into an ordinary convolution.
Our experiments in Table 2 left clearly show that ordinary models by default don't possess the invariance to the absence of the spatial information.
In contrast to the common wisdom, we find that spatial information can be neglected from a significant number of last layers without any performance drop if the invariance is imposed at training, which suggests that spatial information at last layers is not necessary for a good performance.
We should however notice that it doesn't indicate that models whose prediction is based on the spatial information can't generalize well.
Besides, unlike the common design manner that layers at different depth inside the network are normally treated equally, e.g. the same module is always used throughout the architecture, our observation implies it is beneficial to have different designs for different layers since there is no necessity to encode spatial information in the last layers (see Appendix A.3 for discussion on first layers), therefore reducing the model complexity.
Comparing our three methods, we observe that 1x1Conv is more robust to the absence of the spatial information while Shuffle Conv and GAP+FC perform similarly for both VGG-16 and ResNet-50.
This implies that CNNs can still benefit from the larger size of activation maps even though its spatial information is not presented.
To conclude, we empirically show that last layers of CNNs are robust to the absence of the spatial information, which is commonly assumed to be important for object recognition tasks.
Our proposed methods, without accessing any spatial information at last layers of modern CNNs, are able to achieve competitive results on several object recognition datasets incuding CIFAR100, SmallImageNet and ImageNet.
We suggest a good rule of thumb for CNN architectures: using 1x1 convolution or fully connected layers at last layers reduces the number of parameters without affecting the performance.
An interesting future direction is to study whether our methods can generalize to other computer vision tasks, e.g., object detection and pose estimation where the spatial relationships are vital for localizing objects.
A APPENDIX

assistant: text: Spatial information at last layers is not necessary for a good classification accuracy.