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A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition [article]

Harshvardhan Sikka
2020 arXiv   pre-print
This thesis constitutes a research project exploring a generalization of the Variational Autoencoder (VAE), β-VAE, that aims to learn disentangled representations using variational inference. β-VAE incorporates  ...  The ability to recognize objects despite there being differences in appearance, known as Core Object Recognition, forms a critical part of human perception.  ...  Acknowledgments I wish to express my deepest gratitude to everyone whose assistance was indispensable in the completion of this project and my master's degree: Dr. Cengiz  ... 
arXiv:2005.07114v1 fatcat:6754eedw2nbexawoblecfbuuda

Enforcing and Discovering Structure in Machine Learning [article]

Francesco Locatello
2021 arXiv   pre-print
In this dissertation, we consider two different research areas that concern structuring a learning algorithm's solution: when the structure is known and when it has to be discovered.  ...  The world is structured in countless ways.  ...  "A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation".  ... 
arXiv:2111.13693v1 fatcat:2urmfjeh6nhvjeiv3qoiy5hrum

A Survey on Generative Adversarial Networks: Variants, Applications, and Training [article]

Abdul Jabbar, Xi Li, Bourahla Omar
2020 arXiv   pre-print
The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data  ...  Therefore, stable training is a crucial issue in different applications for the success of GAN.  ...  Recently, the Disentangled Representation Net (DRNET) [170] approach learns disentangled image representations from the video.  ... 
arXiv:2006.05132v1 fatcat:gyjezuh5sfdilkp43ydsea5cwa

A Biologically Inspired Visual Working Memory for Deep Networks [article]

Ethan Harris, Mahesan Niranjan, Jonathon Hare
2019 arXiv   pre-print
Classification with the self supervised representation obtained from MNIST is shown to be in line with the state of the art models (none of which use a visual attention mechanism).  ...  As demonstrated through the CelebA dataset, to perform reconstruction the model learns to make a sequence of updates to a canvas which constitute a parts-based representation.  ...  INTRODUCTION Much of the current effort and literature in deep learning focuses on performance from a statistical pattern recognition perspective.  ... 
arXiv:1901.03665v1 fatcat:al4so7l55jhghk7zyumnpb7day

Constrained unsupervised anomaly segmentation [article]

Julio Silva-Rodríguez, Valery Naranjo, Jose Dolz
2022 arXiv   pre-print
Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors  ...  However, a main limitation of nearly all prior literature is the need of employing anomalous images to set a class-specific threshold to locate the anomalies.  ...  The DGX-A100 used in this work was partially funded by Generalitat Valenciana / European Union through the European Regional Development Fund (ERDF) of the Valencian Community (IDIFEDER/2020/030).  ... 
arXiv:2203.01671v1 fatcat:tueshnyaofbw3pv7juv3lqga6u

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition

Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Yongwon Hong, Visvanathan Ramesh
2022 Journal of Imaging  
Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge.  ...  with learned representations.  ...  Materials and Methods Unifying Catastrophic Interference Prevention with Open Set Recognition We first summarize the preliminaries on continual learning from a perspective of variational inference in  ... 
doi:10.3390/jimaging8040093 pmid:35448220 pmcid:PMC9028364 fatcat:gz6u2rxcjzebrifmrzxylla6pe

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [article]

Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser (+29 others)
2021 arXiv   pre-print
Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged.  ...  Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods  ...  Furthermore, this research has been funded by the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01IS18038B).  ... 
arXiv:2104.14235v1 fatcat:f6sj3v2brza7thyzw7b7fkpo2m

Deep Reinforcement Learning: An Overview [article]

Yuxi Li
2018 arXiv   pre-print
We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications.  ...  Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration.  ...  Higgins et al. (2017) proposed β-VAE to automatically discover interpretable, disentangled, factorised, latent representations from raw images in an unsupervised way.  ... 
arXiv:1701.07274v6 fatcat:x2es3yf3crhqblbbskhxelxf2q

Classical Planning in Deep Latent Space [article]

Masataro Asai, Hiroshi Kajino, Alex Fukunaga, Christian Muise
2022 arXiv   pre-print
Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners.  ...  Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment.  ...  Disjunctive conditions are typically compiled away in modern planners at the cost of exponential blowup in the number of ground actions, which a deeper and more accurate tree suffers from.  ... 
arXiv:2107.00110v2 fatcat:24gh3ds5cjfnrmlzlnjp6oaxqm

The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches [article]

Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S. Awwal, Vijayan K. Asari
2018 arXiv   pre-print
The experimental results show state-of-the-art performance of deep learning over traditional machine learning approaches in the field of Image Processing, Computer Vision, Speech Recognition, Machine Translation  ...  Deep learning has demonstrated tremendous success in variety of application domains in the past few years.  ...  Doctoral research scientist on deep Learning, computer vision for remote sensing and hyper spectral imaging (e-mail: pehedings@slu.edu). Brian C Van Esesn 3 and Abdul A S.  ... 
arXiv:1803.01164v2 fatcat:eo353y77tvckbdjcfexpaadeh4

Deep Learning in Protein Structural Modeling and Design [article]

Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
2020 arXiv   pre-print
of a protein, is critical to understand and engineer biological systems at the molecular level.  ...  This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically  ...  Acknowledgement Acknowledgement This work was supported by the National Institutes of Health through grant R01-GM078221.  ... 
arXiv:2007.08383v1 fatcat:ynpdumcqnbel7duwffbork6s2u

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari
2019 Electronics  
This work considers most of the papers published after 2012 from when the history of deep learning began.  ...  In recent years, deep learning has garnered tremendous success in a variety of application domains.  ...  Acknowledgments: We would like to thank all authors mentioned in the reference of this paper from whom we have learned a lot and thus made this review paper possible.  ... 
doi:10.3390/electronics8030292 fatcat:2i64q7g6kjbjvfalvzwgiggnyq

Deep Reinforcement Learning [article]

Yuxi Li
2018 arXiv   pre-print
We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details.  ...  Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation.  ...  Lanctot et al. (2017) observe that independent RL, in which each agent learns by interacting with the environment, oblivious to other agents, can overfit the learned policies to other agents' policies  ... 
arXiv:1810.06339v1 fatcat:kp7atz5pdbeqta352e6b3nmuhy

Information Flow in Deep Neural Networks [article]

Ravid Shwartz-Ziv
2022 arXiv   pre-print
At the end, we present the dual Information Bottleneck (dualIB). This new information-theoretic framework resolves some of the IB's shortcomings by merely switching terms in the distortion function.  ...  An analytical framework reveals the underlying structure and optimal representations, and a variational framework using deep neural network optimization validates the results.  ...  His immense knowledge and plentiful experience have encouraged me in all the time of my academic research and daily life. He was a remarkable man who gave me so much, and I learned a lot from him.  ... 
arXiv:2202.06749v2 fatcat:eo3pcousavg3zp5xza57kejjq4

Deep Learning Techniques for Music Generation – A Survey [article]

Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet
2019 arXiv   pre-print
To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated?  ...  This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature.  ...  The representation is adapted to the objective. At first look, it resembles a piano roll with MIDI note numbers but it is actually a bit different.  ... 
arXiv:1709.01620v4 fatcat:hma4znleorfpvh62cpupxu4fq4
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