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Object Segmentation Without Labels with Large-Scale Generative Models [article]

Andrey Voynov, Stanislav Morozov, Artem Babenko
2021 arXiv   pre-print
This work demonstrates that large-scale unsupervised models can also perform a more challenging object segmentation task, requiring neither pixel-level nor image-level labeling.  ...  By extensive comparison on standard benchmarks, we outperform existing unsupervised alternatives for object segmentation, achieving new state-of-the-art.  ...  Overall, the contributions of our paper are the following: 1. We propose to perform unsupervised object segmentation using off-the-shelf Imagenet-pretrained GANs. 2.  ... 
arXiv:2006.04988v2 fatcat:nt3ae3lbsra3tbqwyu5stoce64

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Walid Ben Ali, Ahmad Pesaranghader, Robert Avram, Pavel Overtchouk, Nils Perrin, Stéphane Laffite, Raymond Cartier, Reda Ibrahim, Thomas Modine, Julie G. Hussin
2021 Frontiers in Cardiovascular Medicine  
Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence  ...  There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy.  ...  Deep Generative Models (DGM) are powerful ways of learning any kind of data distribution using unsupervised learning.  ... 
doi:10.3389/fcvm.2021.711401 pmid:34957230 pmcid:PMC8692711 fatcat:dszezenagrbtzly4a36tbavh3q

Deep Latent-Variable Models for Text Generation [article]

Xiaoyu Shen
2022 arXiv   pre-print
This dissertation presents how deep latent-variable models can improve over the standard encoder-decoder model for text generation.  ...  Nonetheless, deep learning models are known to be extremely data-hungry, and text generated from them usually suffer from low diversity, interpretability and controllability.  ...  Replacing GAN with VAE The idea of AED sounds appealing, but GAN is notoriously difficult to train, especially when both the prior and posterior need to be updated towards each other, the model becomes  ... 
arXiv:2203.02055v1 fatcat:sq3upxl7xvfnhigoc7apszomwu

Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [article]

Jing Zhang, Dacheng Tao
2020 arXiv   pre-print
Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.  ...  However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult.  ...  With the off-the-shelf deep learning tools and scalable computing hardware, it is easy to set up the production environment on the cloud, where deep neural networks are trained and deployed to process  ... 
arXiv:2011.08612v1 fatcat:dflut2wdrjb4xojll34c7daol4

Don't miss the Mismatch: Investigating the Objective Function Mismatch for Unsupervised Representation Learning [article]

Bonifaz Stuhr, Jürgen Brauer
2020 arXiv   pre-print
Thereby we disclose dependencies of the objective function mismatch across several pretext and target tasks with respect to the pretext model's representation size, target model complexity, pretext and  ...  This mismatch states that the performance on a desired target task can decrease when the unsupervised pretext task is learned too long - especially when both tasks are ill-posed.  ...  Acknowledgments Really kindly we want to thank our colleges from the University of Applied Sciences Kempten and the Autonomous University of Barcelona for the helpful discussions about this topic.  ... 
arXiv:2009.02383v1 fatcat:b3qdhjugtrbmbd4uiqegsd26rm

Reinforcement Learning in Practice: Opportunities and Challenges [article]

Yuxi Li
2022 arXiv   pre-print
Then we discuss challenges, in particular, 1) foundation, 2) representation, 3) reward, 4) exploration, 5) model, simulation, planning, and benchmarks, 6) off-policy/offline learning, 7) learning to learn  ...  This article is a gentle discussion about the field of reinforcement learning in practice, about opportunities and challenges, touching a broad range of topics, with perspectives and without technical  ...  A model may be built from a dataset by estimating parameters, and/or with prior (physics) knowledge, or by a generative approach like GANs.  ... 
arXiv:2202.11296v2 fatcat:xdtsmme22rfpfn6rgfotcspnhy

Understanding And Mapping Big Data In Transport Sector

Kim Hee, Naveed Mushtaq, Hevin Özmen, Marten Rosselli, Roberto V. Zicari, Minsung Hong, Rajendra Akerkar, Sophie Roizard, Rémy Russotto, Tharsis Teoh
2018 Zenodo  
It aims to generate a shared understanding of current big data landscape in transport and identifies a holistic view on opportunities, challenges, and limitations.  ...  The remainder of this report is structured as follows: Chapter 2 explores the characteristic of big data and highlights the big data challenges in the transport sector.  ...  Pickup and drop off points were matched to the closest street segments.  ... 
doi:10.5281/zenodo.1465516 fatcat:tqw6cz3uabd75pyuc5wbpxer6u

Affective Image Content Analysis: Two Decades Review and New Perspectives [article]

Sicheng Zhao, Xingxu Yao, Jufeng Yang, Guoli Jia, Guiguang Ding, Tat-Seng Chua, Björn W. Schuller, Kurt Keutzer
2021 arXiv   pre-print
We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison  ...  In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -- the affective  ...  [110] utilized an off-the-shelf object detection tool to generate bounding box candidates.  ... 
arXiv:2106.16125v1 fatcat:5y5y5nhoebccxjjybarnveecgq

Deep Learning in Information Security [article]

Stefan Thaler, Vlado Menkovski, Milan Petkovic
2018 arXiv   pre-print
DL methods generally are capable of achieving high-performance and generalize well. However, information security is a domain with unique requirements and challenges.  ...  Other advantages of DL methods are unrivaled scalability and efficiency, both regarding the number of examples that can be analyzed as well as with respect of dimensionality of the input data.  ...  Objective The objective is a function that defines how the parameters are learned. It tells a model the prediction error towards the ground truth.  ... 
arXiv:1809.04332v1 fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi


2021 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)  
Big data are generated in SPOC learning and reflect how the students learn.  ...  The statistical language models are composed of off-line and online n- grams.  ... 
doi:10.1109/icce-tw52618.2021.9602919 fatcat:aetmvxb7hfah7iuucbamos2wgu

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art [article]

Joel Janai, Fatma Güney, Aseem Behl, Andreas Geiger
2021 arXiv   pre-print
Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes.  ...  As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner.  ...  However, as road segmentation is a subproblem of semantic segmentation, today most state-of-the-art results on road segmentation are achieved using generic off-the-shelf semantic segmentation networks.  ... 
arXiv:1704.05519v3 fatcat:xiintiarqjbfldheeg2hsydyra

ActivityNet Challenge 2017 Summary [article]

Bernard Ghanem, Juan Carlos Niebles, Cees Snoek, Fabian Caba Heilbron, Humam Alwassel, Ranjay Khrisna, Victor Escorcia, Kenji Hata, Shyamal Buch
2017 arXiv   pre-print
The ActivityNet Large Scale Activity Recognition Challenge 2017 Summary: results and challenge participants papers.  ...  We would like to thank the authors of the Kinetics dataset for their kind support; and Joao Carreira and Brian Zhang for helpful discussions.  ...  We also explore some off-the-shelf video segmentation toolkits, but all of them are inferior to the even temporal proposals.  ... 
arXiv:1710.08011v1 fatcat:bc5qhp2cungrdj4j3lebxeoane

Imitating Interactive Intelligence [article]

Josh Abramson, Arun Ahuja, Iain Barr, Arthur Brussee, Federico Carnevale, Mary Cassin, Rachita Chhaparia, Stephen Clark, Bogdan Damoc, Andrew Dudzik, Petko Georgiev, Aurelia Guy (+17 others)
2021 arXiv   pre-print
Finally, we train evaluation models whose ratings of agents agree well with human judgement, thus permitting the evaluation of new agent models without additional effort.  ...  Rigorously evaluating our agents poses a great challenge, so we develop a variety of behavioural tests, including evaluation by humans who watch videos of agents or interact directly with them.  ...  Acknowledgments The authors would like to thank Jay McClelland for formative initial discussions; Paola Jouyaux, Vicky Holgate, Esme Sutherland Robson, Guy Scully, and Alex Goldin for organisational support  ... 
arXiv:2012.05672v2 fatcat:2jeh6widnve55ozlxjunmdi24y

A high-bias, low-variance introduction to Machine Learning for physicists [article]

Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab
2019 arXiv   pre-print
more advanced topics in both supervised and unsupervised learning.  ...  The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to  ...  For this reason, one must be careful when using an off-the-shelf method in high dimensions.  ... 
arXiv:1803.08823v2 fatcat:vmtp62jyvjfxhpidpdcozfnza4

D1.1 - State of the Art Analysis

Danilo Ardagna
2021 Zenodo  
In the last part of the deliverable, we report an overview of the performance modelling solutions, security, and privacy problems for AI applications in edge environments.  ...  It is found that some areas, such as component placement and design space exploration with privacy preservation and performance guarantees, are fairly under-developed, since solutions tailo [...]  ...  The deployment workflow on such devices includes the following steps:• Train the NN model: as for TensorRT, training is performed using off-the-shelf DL frameworks.  ... 
doi:10.5281/zenodo.6372377 fatcat:f6ldfuwivbcltew4smiiwphfty
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