A Style-Based Generator Architecture for Generative Adversarial Networks

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.Comment: CVPR 2019 final versio

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

A Survey on Few-Shot Class-Incremental Learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta-learning based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective

Usando modelos de optimización para alcanzar soluciones en técnicas de clasificación y clusterización

Descargue el texto completo en el repositorio institucional de la Universidade Estadual de Campinas: https://hdl.handle.net/20.500.12733/1641108Esta tesis pretende estudiar algunas técnicas de manejo de conjuntos de datos a gran escala para extraer información representativa a partir del uso de la programación matemática. Los patrones estructurales de los datos proporcionan piezas de información que pueden ser utilizadas para clasificar y agruparlos mediante la solución óptima de problemas de optimización específicos. Las técnicas utilizadas podrían confrontarse con enfoques de aprendizaje automático para suministrar nuevas posibilidades numéricas de resolución. Las pruebas computacionales realizadas sobre dos casos de estudio con datos reales (experimentos prácticos) validan esta investigación. Los análisis se realizan para la conocida base de datos sobre la identificación de tumores de cáncer de mama, que tienen un diagnóstico maligno o benigno, y también para una base de datos de animales bovinos que contiene características físicas y de raza de cada animal pero con patrones desconocidos. Para el primer caso de estudio se propone una clasificación binaria basada en una formulación de programación de objetivos. En el estudio realizado sobre las características de los animales bovinos el interés es identificar patrones entre los diferentes animales agrupándolos a partir de las soluciones de un modelo de optimización lineal entero. Los resultados computacionales se estudian a partir de un conjunto de procedimientos estadísticos descriptivos para validar esta investigación.This dissertation aims to study some techniques for handling large scale datasets to extract representative information from the use of mathematical programming. The structural patterns of data provide pieces of information that can be used to classify and cluster them through the optimal solution of specific optimization problems. The techniques used could be confronted with machine learning approaches to supply new numerical possibilities of resolution. Computational tests conducted on two case studies with real data (practical experiments) validate this research. The analyzes are done for the well-known database on the identification of breast cancer tumors, which either have a malignant or have a benign diagnosis, and also for a bovine animal database containing physical and breed characteristics of each animal but with unknown patterns. A binary classification based on a goal programming formulation is suggested for the first case study. In the study conducted on the characteristics of bovine animals, the interest is to identify patterns among the different animals by grouping them from the solutions of an integer linear optimization model. The computational results are studied from a set of descriptive statistical procedures to validate this research.Brasil. Universidade Estadual de Campinas. Fundação de Desenvolvimento (Funcamp