We consider the problem of binary image generation with given properties. This problem arises in a number of practical applications, including generation of artificial porous medium for an electrode of lithium-ion batteries, for composed materials, etc. A generated image represents a porous medium and, as such, it is subject to two sets of constraints: topological constraints on the structure and process constraints on the physical process over this structure. To perform image generation we need to define a mapping from a porous medium to its physical process parameters. For a given geometry of a porous medium, this mapping can be done by solving a partial differential equation (PDE). However, embedding a PDE solver into the search procedure is computationally expensive. We use a binarized neural network to approximate a PDE solver. This allows us to encode the entire problem as a logical formula. Our main contribution is that, for the first time, we show that this problem can be tackled using decision procedures. Our experiments show that our model is able to produce random constrained images that satisfy both topological and process constraints.
There are two lines of work related to our paper. The first one uses constraint to enhance machine learning techniques with declarative constraints, e.g. in solving constrained clustering problems and in data mining techniques that handle domain specific constraints @cite_22 @cite_19 @cite_0 . One recent example is the work of Ganji @cite_19 who proposed a logical model for constrained community detection. The second line of research explores embedding of domain-specific constraints in the GAN training procedure @cite_6 @cite_24 @cite_9 @cite_7 @cite_18 . Work in this area is targeting various applications in physics and medicine that impose constraints, like sparsity constraints, high dynamic range requirements (e.g. when pixel intensity in an image varies by orders of magnitude), location specificity constraints (e.g. shifting pixel locations can change important image properties), etc. However, this research area is emerging and the results are still preliminary.
@cite_18 @cite_22 @cite_7 @cite_9 @cite_6 @cite_0 @cite_19 @cite_24
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Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose quartet-net learning to improve the discriminative power of CNN features for instance retrieval. The general idea is to map the features into a space where the image similarity can be better evaluated. Our network differs from the traditional Siamese-net in two ways. First, we adopt a double-margin contrastive loss with a dynamic margin tuning strategy to train the network which leads to more robust performance. Second, we introduce in the mimic learning regularization to improve the generalization ability of the network by preventing it from overfitting to the training data. Catering for the network learning, we collect a large-scale dataset, namely GeoPair, which consists of 68k matching image pairs and 63k non-matching pairs. Experiments on several standard instance retrieval datasets demonstrate the effectiveness of our method. Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. Our method consists of both global and category specific adaptation techniques. Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. This initially adapted space then enables category specific adaptation through a generalization of constrained weak learning, with explicit transfer of the spatial layout from the source to the target domains. Our approach outperforms baselines across different settings on multiple large-scale datasets, including adapting across various real city environments, different synthetic sub-domains, from simulated to real environments, and on a novel large-scale dash-cam dataset. Estimating depth from a single image is a very challenging and exciting topic in computer vision with implications in several application domains. Recently proposed deep learning approaches achieve outstanding results by tackling it as an image reconstruction task and exploiting geometry constraints (e.g., epipolar geometry) to obtain supervisory signals for training. Inspired by these works and compelling results achieved by Generative Adversarial Network (GAN) on image reconstruction and generation tasks, in this paper we propose to cast unsupervised monocular depth estimation within a GAN paradigm. The generator network learns to infer depth from the reference image to generate a warped target image. At training time, the discriminator network learns to distinguish between fake images generated by the generator and target frames acquired with a stereo rig. To the best of our knowledge, our proposal is the first successful attempt to tackle monocular depth estimation with a GAN paradigm and the extensive evaluation on CityScapes and KITTI datasets confirm that it enables to improve traditional approaches. Additionally, we highlight a major issue with data deployed by a standard evaluation protocol widely used in this field and fix this problem using a more reliable dataset recently made available by the KITTI evaluation benchmark. The main contribution of this paper is a simple semi-supervised pipeline that only uses the original training set without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regularization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which regularizes the supervised model and improves the baseline. We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retrieve a query person from other cameras. We adopt the deep convolutional generative adversarial network (DCGAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and DukeMTMC-reID, we obtain +4.37 , +1.6 and +2.46 improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6 improvement over a strong baseline. The code is available at this https URL