Sum-Product Networks (SPNs) are hierarchical, probabilistic graphical models capable of fast and exact inference that can be trained directly from high-dimensional, noisy data. Traditionally, SPNs struggle with capturing relationships in complex spatial data such as images. To this end, we introduce Deep Generalized Convolutional Sum-Product Networks (DGC-SPNs), which encode spatial features through products and sums with scopes corresponding to local receptive fields. As opposed to existing convolutional SPNs, DGC-SPNs allow for overlapping convolution patches through a novel parameterization of dilation and strides, resulting in significantly improved feature coverage and feature resolution. DGC-SPNs substantially outperform other convolutional and non-convolutional SPN approaches across several visual datasets and for both generative and discriminative tasks, including image completion and image classification. In addition, we demonstrate a modificiation to hard EM learning that further improves the generative performance of DGC-SPNs. While fully probabilistic and versatile, our model is scalable and straightforward to apply in practical applications in place of traditional deep models. Our implementation is tensorized, employs efficient GPU-accelerated optimization techniques, and is available as part of an open-source library based on TensorFlow.
In @cite_19 , image completion was performed on the Olivetti @cite_4 and Caltech @cite_6 datasets showing superior results compared to other generative models.
@cite_19 @cite_4 @cite_6
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In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach (). Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40 of the time, compared to 10 for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset. In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40 of the time, compared to 10 for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset. Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7 of the classes have samples exhibiting diversity comparable to real ImageNet data. Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7 of the classes have samples exhibiting diversity comparable to real ImageNet data.