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Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence [article]

Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, Errui Ding
2022 arXiv   pre-print
Using joint optimization of both directions, the proposed model is able to learn upscaling and downscaling simultaneously and achieve bidirectional arbitrary image rescaling.  ...  This robustness is beneficial for image rescaling in the wild when this cycle could be applied to one image for multiple times.  ...  in arbitrary image rescaling via joint optimization. • A proxy objective that optimize both reconstruction accuracy and idempotence is investigated, and a newly proposed cycle idempotence test is conducted  ... 
arXiv:2203.00911v2 fatcat:taln5p5jqfbgfklrijlek5huhq

Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition

Rama Chellappa
2009 Foundations and Trends® in Signal Processing  
His books explore the use of group theoretical methods [106] and statistical optimization [107] in image understanding and computer vision.  ...  Kanatani pioneered statistical optimization under the constraints unique to vision problems.  ...  The third author wishes to thank the members of the imaging group at MERL for their support.  ... 
doi:10.1561/2000000007 fatcat:o5hmdnzbqvbdzjdu72jkojl5ya

25th Annual Computational Neuroscience Meeting: CNS-2016

Tatyana O. Sharpee, Alain Destexhe, Mitsuo Kawato, Vladislav Sekulić, Frances K. Skinner, Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári, Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett (+597 others)
2016 BMC Neuroscience  
Such classification scheme could augment classification schemes based on molecular, anatomical, and electrophysiological properties.  ...  I will discuss theoretical results that point to functional advantages of splitting neural populations into subtypes, both in feedforward and recurrent networks.  ...  Allen and Jody Allen, for their vision, encouragement and support.  ... 
doi:10.1186/s12868-016-0283-6 pmid:27534393 pmcid:PMC5001212 fatcat:bt45etzj2bbolfcxlxo7hlv6ju

Machine learning with neural networks [article]

B. Mehlig
2021 arXiv   pre-print
Lecture notes for my course on machine learning with neural networks that I have given at Gothenburg University and Chalmers Technical University in Gothenburg, Sweden.  ...  Using experience in this way allows the algorithm to simultaneously improve its policy and the Q -values towards optimality.  ...  The norm of the weight vector, in particular, is arbitrary.  ... 
arXiv:1901.05639v3 fatcat:pyyiywuoxzds5kyc6ohqtqtd3e

Diversity-Promoting and Large-Scale Machine Learning for Healthcare

Pengtao Xie
In the former, we develop diversity-promoting regularizers that are empirically effective, theoretically analyzable, and computationally efficient, and proposea rich set of optimization algorithms to solve  ...  In healthcare, a tsunami of medical data has emerged, including electronic healthrecords, images, literature, etc.  ...  Teng and Zhang [330] extended the unidirectional tree LSTM to a bidirectional one. Xie and Xing [368] proposed a sequence-of-trees LSTM network to model a passage.  ... 
doi:10.1184/r1/7553468 fatcat:ac5ifp2lnzbk3hcupr2rszxj2m