628 research outputs found

    Persistently Trained, Diffusion-assisted Energy-based Models

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    Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation. We propose to introduce diffusion data and learn a joint EBM, called diffusion assisted-EBMs, through persistent training (i.e., using persistent contrastive divergence) with an enhanced sampling algorithm to properly sample from complex, multimodal distributions. We present results from a 2D illustrative experiment and image experiments and demonstrate that, for the first time for image data, persistently trained EBMs can {\it simultaneously} achieve long-run stability, post-training image generation, and superior out-of-distribution detection.Comment: main text 8 page

    Improving Deep Representation Learning with Complex and Multimodal Data.

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    Representation learning has emerged as a way to learn meaningful representation from data and made a breakthrough in many applications including visual object recognition, speech recognition, and text understanding. However, learning representation from complex high-dimensional sensory data is challenging since there exist many irrelevant factors of variation (e.g., data transformation, random noise). On the other hand, to build an end-to-end prediction system for structured output variables, one needs to incorporate probabilistic inference to properly model a mapping from single input to possible configurations of output variables. This thesis addresses limitations of current representation learning in two parts. The first part discusses efficient learning algorithms of invariant representation based on restricted Boltzmann machines (RBMs). Pointing out the difficulty of learning, we develop an efficient initialization method for sparse and convolutional RBMs. On top of that, we develop variants of RBM that learn representations invariant to data transformations such as translation, rotation, or scale variation by pooling the filter responses of input data after a transformation, or to irrelevant patterns such as random or structured noise, by jointly performing feature selection and feature learning. We demonstrate improved performance on visual object recognition and weakly supervised foreground object segmentation. The second part discusses conditional graphical models and learning frameworks for structured output variables using deep generative models as prior. For example, we combine the best properties of the CRF and the RBM to enforce both local and global (e.g., object shape) consistencies for visual object segmentation. Furthermore, we develop a deep conditional generative model of structured output variables, which is an end-to-end system trainable by backpropagation. We demonstrate the importance of global prior and probabilistic inference for visual object segmentation. Second, we develop a novel multimodal learning framework by casting the problem into structured output representation learning problems, where the output is one data modality to be predicted from the other modalities, and vice versa. We explain as to how our method could be more effective than maximum likelihood learning and demonstrate the state-of-the-art performance on visual-text and visual-only recognition tasks.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113549/1/kihyuks_1.pd

    Long-term future prediction under uncertainty and multi-modality

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    Humans have an innate ability to excel at activities that involve prediction of complex object dynamics such as predicting the possible trajectory of a billiard ball after it has been hit by the player or the prediction of motion of pedestrians while on the road. A key feature that enables humans to perform such tasks is anticipation. There has been continuous research in the area of Computer Vision and Artificial Intelligence to mimic this human ability for autonomous agents to succeed in the real world scenarios. Recent advances in the field of deep learning and the availability of large scale datasets has enabled the pursuit of fully autonomous agents with complex decision making abilities such as self-driving vehicles or robots. One of the main challenges encompassing the deployment of these agents in the real world is their ability to perform anticipation tasks with at least human level efficiency. To advance the field of autonomous systems, particularly, self-driving agents, in this thesis, we focus on the task of future prediction in diverse real world settings, ranging from deterministic scenarios such as prediction of paths of balls on a billiard table to the predicting the future of non-deterministic street scenes. Specifically, we identify certain core challenges for long-term future prediction: long-term prediction, uncertainty, multi-modality, and exact inference. To address these challenges, this thesis makes the following core contributions. Firstly, for accurate long-term predictions, we develop approaches that effectively utilize available observed information in the form of image boundaries in videos or interactions in street scenes. Secondly, as uncertainty increases into the future in case of non-deterministic scenarios, we leverage Bayesian inference frameworks to capture calibrated distributions of likely future events. Finally, to further improve performance in highly-multimodal non-deterministic scenarios such as street scenes, we develop deep generative models based on conditional variational autoencoders as well as normalizing flow based exact inference methods. Furthermore, we introduce a novel dataset with dense pedestrian-vehicle interactions to further aid the development of anticipation methods for autonomous driving applications in urban environments.Menschen haben die angeborene Fähigkeit, Vorgänge mit komplexer Objektdynamik vorauszusehen, wie z. B. die Vorhersage der möglichen Flugbahn einer Billardkugel, nachdem sie vom Spieler gestoßen wurde, oder die Vorhersage der Bewegung von Fußgängern auf der Straße. Eine Schlüsseleigenschaft, die es dem Menschen ermöglicht, solche Aufgaben zu erfüllen, ist die Antizipation. Im Bereich der Computer Vision und der Künstlichen Intelligenz wurde kontinuierlich daran geforscht, diese menschliche Fähigkeit nachzuahmen, damit autonome Agenten in der realen Welt erfolgreich sein können. Jüngste Fortschritte auf dem Gebiet des Deep Learning und die Verfügbarkeit großer Datensätze haben die Entwicklung vollständig autonomer Agenten mit komplexen Entscheidungsfähigkeiten wie selbstfahrende Fahrzeugen oder Roboter ermöglicht. Eine der größten Herausforderungen beim Einsatz dieser Agenten in der realen Welt ist ihre Fähigkeit, Antizipationsaufgaben mit einer Effizienz durchzuführen, die mindestens der menschlichen entspricht. Um das Feld der autonomen Systeme, insbesondere der selbstfahrenden Agenten, voranzubringen, konzentrieren wir uns in dieser Arbeit auf die Aufgabe der Zukunftsvorhersage in verschiedenen realen Umgebungen, die von deterministischen Szenarien wie der Vorhersage der Bahnen von Kugeln auf einem Billardtisch bis zur Vorhersage der Zukunft von nicht-deterministischen Straßenszenen reichen. Insbesondere identifizieren wir bestimmte grundlegende Herausforderungen für langfristige Zukunftsvorhersagen: Langzeitvorhersage, Unsicherheit, Multimodalität und exakte Inferenz. Um diese Herausforderungen anzugehen, leistet diese Arbeit die folgenden grundlegenden Beiträge. Erstens: Für genaue Langzeitvorhersagen entwickeln wir Ansätze, die verfügbare Beobachtungsinformationen in Form von Bildgrenzen in Videos oder Interaktionen in Straßenszenen effektiv nutzen. Zweitens: Da die Unsicherheit in der Zukunft bei nicht-deterministischen Szenarien zunimmt, nutzen wir Bayes’sche Inferenzverfahren, um kalibrierte Verteilungen wahrscheinlicher zukünftiger Ereignisse zu erfassen. Drittens: Um die Leistung in hochmultimodalen, nichtdeterministischen Szenarien wie Straßenszenen weiter zu verbessern, entwickeln wir tiefe generative Modelle, die sowohl auf konditionalen Variations-Autoencodern als auch auf normalisierenden fließenden exakten Inferenzmethoden basieren. Darüber hinaus stellen wir einen neuartigen Datensatz mit dichten Fußgänger-Fahrzeug- Interaktionen vor, um Antizipationsmethoden für autonome Fahranwendungen in urbanen Umgebungen weiter zu entwickeln

    Conditional Invertible Generative Models for Supervised Problems

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    Invertible neural networks (INNs), in the setting of normalizing flows, are a type of unconditional generative likelihood model. Despite various attractive properties compared to other common generative model types, they are rarely useful for supervised tasks or real applications due to their unguided outputs. In this work, we therefore present three new methods that extend the standard INN setting, falling under a broader category we term generative invertible models. These new methods allow leveraging the theoretical and practical benefits of INNs to solve supervised problems in new ways, including real-world applications from different branches of science. The key finding is that our approaches enhance many aspects of trustworthiness in comparison to conventional feed-forward networks, such as uncertainty estimation and quantification, explainability, and proper handling of outlier data
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