Why do These Match? Explaining the Behavior of Image Similarity Models

Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models for tasks like image classification or visual question answering. In this paper, we introduce Salient Attributes for Network Explanation (SANE) to explain image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification score. In this task, an explanation depends on both of the input images, so standard methods do not apply. Our SANE explanations pairs a saliency map identifying important image regions with an attribute that best explains the match. We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition. Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2. Code available at: https://github.com/VisionLearningGroup/SANEComment: Accepted at ECCV 202

HandsOff: Labeled Dataset Generation With No Additional Human Annotations

Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of generated labels. We introduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and corresponding labels after being trained on less than 50 pre-existing labeled images. Our framework avoids the practical drawbacks of prior work by unifying the field of GAN inversion with dataset generation. We generate datasets with rich pixel-wise labels in multiple challenging domains such as faces, cars, full-body human poses, and urban driving scenes. Our method achieves state-of-the-art performance in semantic segmentation, keypoint detection, and depth estimation compared to prior dataset generation approaches and transfer learning baselines. We additionally showcase its ability to address broad challenges in model development which stem from fixed, hand-annotated datasets, such as the long-tail problem in semantic segmentation.Comment: 22 pages, 20 figure

Association between community noise and adiposity in patients with cardiovascular disease

Introduction: This study aimed to explore the effect of community noise on body mass index (BMI) and waist circumference (WC) in patients with cardiovascular disease (CVD). Materials and Methods: A representative sample of 132 patients from three tertiary hospitals in the city of Plovdiv, Bulgaria was collected. Anthropometric measurements were linked to global noise annoyance (GNA) based on different residential noise annoyances, day–evening–night (Lden), and nighttime (Lnight) road traffic noise exposure. Noise map Lden and Lnight were determined at the living room and bedroom façades, respectively, and further corrected to indoor exposure based on the window-opening frequency and soundproofing insulation. Results and Discussion: Results showed that BMI and WC increased (non-significantly) per 5 dB. The effect of indoor noise was stronger in comparison with that of outdoor noise. For indoor Lden, the effect was more pronounced in men, those with diabetes, family history of diabetes, high noise sensitivity, using solid fuel/gas for domestic heating/cooking, and living on the first floor. As regards indoor Lnight, its effect was more pronounced in those with low socioeconomic status, hearing loss, and using solid fuel/gas for domestic heating/cooking. GNA was associated with lower BMI and WC. Conclusion: Road traffic noise was associated with an increase in adiposity in some potentially vulnerable patients with CVD

Abstracts Of The Proceedings And The Posters From The Third Scientific Session Of The Medical College Of Varna

October 2-3, 201