Image attribute editing is a challenging problem that has been recently studied by many researchers using generative networks. The challenge is in the manipulation of selected attributes of images while preserving the other details. The method to achieve this goal is to find an accurate latent vector representation of an image and a direction corresponding to the attribute. Almost all the works in the literature use labeled datasets in a supervised setting for this purpose. In this study, we introduce an architecture called Cyclic Reverse Generator (CRG), which allows learning the inverse function of the generator accurately via an encoder in an unsupervised setting by utilizing cyclic cost minimization. Attribute editing is then performed using the CRG models for finding desired attribute representations in the latent space. In this work, we use two arbitrary reference images, with and without desired attributes, to compute an attribute direction for editing. We show that the proposed approach performs better in terms of image reconstruction compared to the existing end-to-end generative models both quantitatively and qualitatively. We demonstrate state-of-the-art results on both real images and generated images in MNIST and CelebA datasets.
Global Pooling (GP) is one of the important layers in deep neural networks. GP significantly reduces the number of model parameters by summarizing the feature maps and enables a reduction in the computational cost of training. The most commonly used GP methods are global max pooling (GMP) and global average pooling (GAP). The GMP method produces successful results in experimental studies but has a tendency to overfit training data and may not generalize well to test data. On the other hand, the GAP method takes into account all activations in the pooling region, which reduces the effect of high activation areas and causes a decrease in model performance. In this study, a GP method called global average of top-k max pooling (GAMP) is proposed, which returns the average of the highest k activations in the feature map and allows for mixing the two methods mentioned. The proposed method is compared quantitatively with other GP methods using different models, i.e., Custom and VGG16-based and different datasets, i.e., CIFAR10 and CIFAR100. The experimental results show that the proposed GAMP method provides better image classification accuracy than the other GP methods. When the Custom model is used, the proposed GAMP method provides a classification accuracy of 1.29% higher on the CIFAR10 dataset and 1.72% higher on the CIFAR100 dataset compared to the method with the closest performance.
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