For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, and tolerance for image transformations was primarily achieved by using robust feature vectors. In this paper we propose a novel and computationally efficient way to learn a local Mahalanobis metric per datum, and show how we can learn a local invariant metric to any transformation in order to improve performance.
The most common way to achieve invariance, or at least insensitivity, to a transformation in computer vision applications is by using hand-crafted descriptors such as SIFT @cite_7 or HOG @cite_0 . Another way, used in convolutional networks @cite_14 , is by adding pooling and subsampling forcing the net to be insensitive to small translations. It is important to note that transformations such as rotations have a global behaviour, i.e. there is a global consistency between the pixel movement. This global consistency is not totally captured by the pooling and subsampling. As we will see in our experiments, using an invariant metric can be useful even when working with robust features such as HOG.
@cite_0 @cite_14 @cite_7
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Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input image translations produce proportionate feature map translations. This is not the case for rotations. Global rotation equivariance is typically sought through data augmentation, but patch-wise equivariance is more difficult. We present Harmonic Networks or H-Nets, a CNN exhibiting equivariance to patch-wise translation and 360-rotation. We achieve this by replacing regular CNN filters with circular harmonics, returning a maximal response and orientation for every receptive field patch. H-Nets use a rich, parameter-efficient and fixed computational complexity representation, and we show that deep feature maps within the network encode complicated rotational invariants. We demonstrate that our layers are general enough to be used in conjunction with the latest architectures and techniques, such as deep supervision and batch normalization. We also achieve state-of-the-art classification on rotated-MNIST, and competitive results on other benchmark challenges. Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input image translations produce proportionate feature map translations. This is not the case for rotations. Global rotation equivariance is typically sought through data augmentation, but patch-wise equivariance is more difficult. We present Harmonic Networks or H-Nets, a CNN exhibiting equivariance to patch-wise translation and 360-rotation. We achieve this by replacing regular CNN filters with circular harmonics, returning a maximal response and orientation for every receptive field patch. H-Nets use a rich, parameter-efficient and low computational complexity representation, and we show that deep feature maps within the network encode complicated rotational invariants. We demonstrate that our layers are general enough to be used in conjunction with the latest architectures and techniques, such as deep supervision and batch normalization. We also achieve state-of-the-art classification on rotated-MNIST, and competitive results on other benchmark challenges. Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods based on kernel rotation and flip to enable rotation and flip invariance in convolutional neural networks. The kernel rotation can be achieved on kernels of 3 × 3, while kernel flip can be applied on kernels of any size. By rotating in eight or four angles, the convolutional layers could produce the corresponding number of feature maps based on eight or four different kernels. By using flip, the convolution layer can produce three feature maps. By combining produced feature maps using maxout, the resource requirement could be significantly reduced while still retain the invariance properties. Experimental results demonstrate that the proposed methods can achieve various invariance at reasonable resource requirements in terms of both memory and time. Identifying suitable image features is a central challenge in computer vision, ranging from representations for low-level to high-level vision. Due to the difficulty of this task, techniques for learning features directly from example data have recently gained attention. Despite significant benefits, these learned features often have many fewer of the desired invariances or equivariances than their hand-crafted counterparts. While translation in- equivariance has been addressed, the issue of learning rotation-invariant or equivariant representations is hardly explored. In this paper we describe a general framework for incorporating invariance to linear image transformations into product models for feature learning. A particular benefit is that our approach induces transformation-aware feature learning, i.e. it yields features that have a notion with which specific image transformation they are used. We focus our study on rotation in- equivariance and show the advantages of our approach in learning rotation-invariant image priors and in building rotation-equivariant and invariant descriptors of learned features, which result in state-of-the-art performance for rotation-invariant object detection.