Semi-Supervised Video and Image in-painting

Human brain is inherently good at pattern recognition. AI researchers have always struggled to emulate such levels of performance in machine vision algorithms. Building as an extension of the pattern recognition, humans are also exceptional in selectively learning and bridging the gap when there is missing data. Even when the missing data is in complex images and videos formats. Human brain is able to surmise and comprehend the scene with reasonable certainty given enough contextual information. We believe that selective learning aids in selectively filling the missing data. To this end we experiment with partial convolutions, and the networks can learn selectively. The idea behind partial convolutions is simple. We use the semantic segmentation masks which are obtained from our novel semantic segmentation network and apply convolutions only on the unmasked pixels of the images. When the image embedding is obtained at the end of the encoder, the data from the masked region of the image will be absent in the image embedding. This is encoded onto the latent vector space. When the images are rebuilt again by from the embedding with a decoder network, the object in the masked region is removed. Furthermore, the problem of image/video in-painting are reformulated as a domain transfer problem. This facilitates our network to be trained as semi-supervised learning. Our network uses less computation power while training semi-supervised, end-to-end and while offering performance close to the current state of the art. We test our network extensively on different datasets. The results while experimenting with partial convolutions and selective learning network have been promising. We have used places2 and cityscapes dataset to experiment on images and Davis 2017 Dataset as video dataset

Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning

The success of deep learning in computer vision is rooted in the ability of deep networks to scale up model complexity as demanded by challenging visual tasks. As complexity is increased, so is the need for large amounts of labeled data to train the model. This is associated with a costly human annotation effort. To address this concern, with the long-term goal of leveraging the abundance of cheap unlabeled data, we explore methods of unsupervised "pre-training." In particular, we propose to use self-supervised automatic image colorization. We show that traditional methods for unsupervised learning, such as layer-wise clustering or autoencoders, remain inferior to supervised pre-training. In search for an alternative, we develop a fully automatic image colorization method. Our method sets a new state-of-the-art in revitalizing old black-and-white photography, without requiring human effort or expertise. Additionally, it gives us a method for self-supervised representation learning. In order for the model to appropriately re-color a grayscale object, it must first be able to identify it. This ability, learned entirely self-supervised, can be used to improve other visual tasks, such as classification and semantic segmentation. As a future direction for self-supervision, we investigate if multiple proxy tasks can be combined to improve generalization. This turns out to be a challenging open problem. We hope that our contributions to this endeavor will provide a foundation for future efforts in making self-supervision compete with supervised pre-training.Comment: Ph.D. thesi

Data Reduction and Deep-Learning Based Recovery for Geospatial Visualization and Satellite Imagery

The storage, retrieval and distribution of data are some critical aspects of big data management. Data scientists and decision-makers often need to share large datasets and make decisions on archiving or deleting historical data to cope with resource constraints. As a consequence, there is an urgency of reducing the storage and transmission requirement. A potential approach to mitigate such problems is to reduce big datasets into smaller ones, which will not only lower storage requirements but also allow light load transfer over the network. The high dimensional data often exhibit high repetitiveness and paradigm across different dimensions. Carefully prepared data by removing redundancies, along with a machine learning model capable of reconstructing the whole dataset from its reduced version, can improve the storage scalability, data transfer, and speed up the overall data management pipeline. In this thesis, we explore some data reduction strategies for big datasets, while ensuring that the data can be transferred and used ubiquitously by all stakeholders, i.e., the entire dataset can be reconstructed with high quality whenever necessary. One of our data reduction strategies follows a straightforward uniform pattern, which guarantees a minimum of 75% data size reduction. We also propose a novel variance based reduction technique, which focuses on removing only redundant data and offers additional 1% to 2% deletion rate. We have adopted various traditional machine learning and deep learning approaches for high-quality reconstruction. We evaluated our pipelines with big geospatial data and satellite imageries. Among them, our deep learning approaches have performed very well both quantitatively and qualitatively with the capability of reconstructing high quality features. We also show how to leverage temporal data for better reconstruction. For uniform deletion, the reconstruction accuracy observed is as high as 98.75% on an average for spatial meteorological data (e.g., soil moisture and albedo), and 99.09% for satellite imagery. Pushing the deletion rate further by following variance based deletion method, the decrease in accuracy remains within 1% for spatial meteorological data and 7% for satellite imagery

Recovering missing data from human body images

We establish critical discussion on the problem of Semantic Inpainting in still images of humans. We present a Dataset for this task as well as analyze the performance of current SOTA methods on it. We also present a novel metric based on human pose estimation quality over the reconstruction

Learning Generalizable Visual Patterns Without Human Supervision

Owing to the existence of large labeled datasets, Deep Convolutional Neural Networks have ushered in a renaissance in computer vision. However, almost all of the visual data we generate daily - several human lives worth of it - remains unlabeled and thus out of reach of today’s dominant supervised learning paradigm. This thesis focuses on techniques that steer deep models towards learning generalizable visual patterns without human supervision. Our primary tool in this endeavor is the design of Self-Supervised Learning tasks, i.e., pretext-tasks for which labels do not involve human labor. Besides enabling the learning from large amounts of unlabeled data, we demonstrate how self-supervision can capture relevant patterns that supervised learning largely misses. For example, we design learning tasks that learn deep representations capturing shape from images, motion from video, and 3D pose features from multi-view data. Notably, these tasks’ design follows a common principle: The recognition of data transformations. The strong performance of the learned representations on downstream vision tasks such as classification, segmentation, action recognition, or pose estimation validate this pretext-task design. This thesis also explores the use of Generative Adversarial Networks (GANs) for unsupervised representation learning. Besides leveraging generative adversarial learning to define image transformation for self-supervised learning tasks, we also address training instabilities of GANs through the use of noise. While unsupervised techniques can significantly reduce the burden of supervision, in the end, we still rely on some annotated examples to fine-tune learned representations towards a target task. To improve the learning from scarce or noisy labels, we describe a supervised learning algorithm with improved generalization in these challenging settings

Improved training of generative models

Cette thèse explore deux idées différentes: — Une méthode améliorée d’entraînement de réseaux de neurones récurrents. Communément, l’entraînement des réseaux de neurones récurrents se fait à l’aide d’une méthode connue sous le nom de ‘teacher forcing’. Cette méthode consiste à utiliser les valeurs de la séquence observée en tant qu’entrées du réseau pendant la phase d’entraînement, alors que l’on utilise la séquence des valeurs prédites par le modèle lors de la phase de génération. Nous présentons ici un algorithme appelé ‘professor forcing’ qui utilise l’adaptation de domaine adversaire pour encourager la dynamique du réseau récurrent à être la même lors de la phase d’entraînement et lors de la phase de génération. Ce travail a été accepté a la session de posters de la conférence NIPS 2016. — Un nouveau modèle pour l’entraînement de modèles génératifs. Un obstacle connu lors de l’entraînement de modèles graphiques non orientés avec variables latentes, tels que les machines de Boltzmann, est que la procédure d’entraînement par maximum de vraisemblance nécessite une chaîne de Markov pour échantillonner. Or le temps de mixage de la chaîne de Markov dans la boucle interne de l’entraînement peut être très long. Dans cette thèse, nous proposons d’abord l’idée qu’il suffit de découper localement la fonction d´énergie de sorte que son gradient pointe dans la bonne direction (c'est-à-dire vers la génération des données). Cela correspond à une nouvelle procédure d’apprentissage qui s’éloigne d’abord des données en suivant l’opérateur de transition du modèle, et qui ensuite entraîne cet opérateur à revenir en arrière à chaque étape, en revenant vers les données. Ce travail a été accepté en tant que poster à la conférence NIPS 2017. Dans le premier chapitre, je présente quelques notions élémentaires sur les modèles génératifs (en particulier les modèles graphiques orientés et non orientés). Je montre en quoi la méthode proposée dans le chapitre 3 est liée à ces modèles. Dans le deuxième chapitre, je décris notre méthode proposée (appelée ‘professor forcing’) pour améliorer l’entraînement des réseaux de neurones récurrents. Dans le troisième chapitre, je décris notre méthode proposée pour entraîner un modèle génératif en paramétrant directement un opérateur de transition.This thesis explores ideas along 2 different directions: — Improved Training of Recurrent Neural Networks - Recurrent Neural Networks are trained using teacher forcing which works by supplying observed sequence values as inputs during training, and using the network’s own one-step ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. This work was accepted as a conference poster at NIPS 2016. — Training iterative generative models A recognized obstacle to training undirected graphical models with latent variables such as Boltzmann machines is that the maximum likelihood training procedure requires sampling from Monte-Carlo Markov chains which may not mix well, in the inner loop of training, for each example. In this thesis, we first propose the idea that it is sufficient to locally carve the energy function everywhere so that its gradient points in the right direction (i.e., towards generating the data). This corresponds to a new learning procedure that first walks away from data points by following the model transition operator and then trains that operator to walk backwards for each of these steps, back towards the training example. This work was accepted as a conference poster at NIPS 2017. Chapter One is dedicated to background knowledge about generative models. This covers directed and undirectored graphical models and how the proposed method in Chapter 3 are related to these. In the following chapter, I will describe our proposed method to improve training of recurrent neural networks using Professor Forcing Goyal et al. [2016]. The third chapter describes the Variational Walkback [Goyal et al., 2017a] algorithm. This is an algorithm for training an iterative generative model by directly learns a parameterized transition operator

Understanding a Dynamic World: Dynamic Motion Estimation for Autonomous Driving Using LIDAR

In a society that is heavily reliant on personal transportation, autonomous vehicles present an increasingly intriguing technology. They have the potential to save lives, promote efficiency, and enable mobility. However, before this vision becomes a reality, there are a number of challenges that must be solved. One key challenge involves problems in dynamic motion estimation, as it is critical for an autonomous vehicle to have an understanding of the dynamics in its environment for it to operate safely on the road. Accordingly, this thesis presents several algorithms for dynamic motion estimation for autonomous vehicles. We focus on methods using light detection and ranging (LIDAR), a prevalent sensing modality used by autonomous vehicle platforms, due to its advantages over other sensors, such as cameras, including lighting invariance and fidelity of 3D geometric data. First, we propose a dynamic object tracking algorithm. The proposed method takes as input a stream of LIDAR data from a moving object collected by a multi-sensor platform. It generates an estimate of its trajectory over time and a point cloud model of its shape. We formulate the problem similarly to simultaneous localization and mapping (SLAM), allowing us to leverage existing techniques. Unlike prior work, we properly handle a stream of sensor measurements observed over time by deriving our algorithm using a continuous-time estimation framework. We evaluate our proposed method on a real-world dataset that we collect. Second, we present a method for scene flow estimation from a stream of LIDAR data. Inspired by optical flow and scene flow from the computer vision community, our framework can estimate dynamic motion in the scene without relying on segmentation and data association while still rivaling the results of state-of-the-art object tracking methods. We design our algorithms to exploit a graphics processing unit (GPU), enabling real-time performance. Third, we leverage deep learning tools to build a feature learning framework that allows us to train an encoding network to estimate features from a LIDAR occupancy grid. The learned feature space describes the geometric and semantic structure of any location observed by the LIDAR data. We formulate the training process so that distances in this learned feature space are meaningful in comparing the similarity of different locations. Accordingly, we demonstrate that using this feature space improves our estimate of the dynamic motion in the environment over time. In summary, this thesis presents three methods to aid in understanding a dynamic world for autonomous vehicle applications with LIDAR. These methods include a novel object tracking algorithm, a real-time scene flow estimation method, and a feature learning framework to aid in dynamic motion estimation. Furthermore, we demonstrate the performance of all our proposed methods on a collection of real-world datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147587/1/aushani_1.pd