Search Results for author: Song Zhang

Found 7 papers, 0 papers with code

Learning to See in the Dark with Events

no code implementations ECCV 2020 Song Zhang, Yu Zhang, Zhe Jiang, Dongqing Zou, Jimmy Ren, Bin Zhou

A detail enhancing branch is proposed to reconstruct day light-specific features from the domain-invariant representations in a residual manner, regularized by a ranking loss.

Representation Learning Unsupervised Domain Adaptation

Practical Adoption of Cloud Computing in Power Systems- Drivers, Challenges, Guidance, and Real-world Use Cases

no code implementations31 Jul 2021 Song Zhang, Amritanshu Pandey, Xiaochuan Luo, Maggy Powell, Ranjan Banerji, Lei Fan, Abhineet Parchure, Edgardo Luzcando

Motivated by FERC's recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs.

Unperturbed inverse kinematics nucleon knockout measurements with a 48 GeV/c carbon beam

no code implementations4 Feb 2021 M. Patsyuk, J. Kahlbow, G. Laskaris, M. Duer, V. Lenivenko, E. P. Segarra, T. Atovullaev, G. Johansson, T. Aumann, A. Corsi, O. Hen, M. Kapishin, V. Panin, E. Piasetzky, Kh. Abraamyan, S. Afanasiev, G. Agakishiev, P. Alekseev, E. Atkin, T. Aushev, V. Babkin, V. Balandin, D. Baranov, N. Barbashina, P. Batyuk, S. Bazylev, A. Beck, C. A. Bertulani, D. Blaschke, D. Blau, D. Bogoslovsky, A. Bolozdynya, K. Boretzky, V. Burtsev, M. Buryakov, S. Buzin, A. Chebotov, J. Chen, A. Ciszewski, R. Cruz-Torres, B. Dabrowska, D. Dabrowski A. Dmitriev, A. Dryablov, P. Dulov, D. Egorov, A. Fediunin, I. Filippov, K. Filippov, D. Finogeev, I. Gabdrakhmanov, A. Galavanov, I. Gasparic, O. Gavrischuk, K. Gertsenberger, A. Gillibert, V. Golovatyuk, M. Golubeva, F. Guber, Yu. Ivanova, A. Ivashkin, A. Izvestnyy, S. Kakurin, V. Karjavin, N. Karpushkin, R. Kattabekov, V. Kekelidze, S. Khabarov, Yu. Kiryushin, A. Kisiel, V. Kolesnikov, A. Kolozhvari, Yu. Kopylov, I. Korover, L. Kovachev, A. Kovalenko, Yu. Kovalev, A. Kugler, S. Kuklin, E. Kulish, A. Kuznetsov, E. Ladygin, N. Lashmanov, E. Litvinenko, S. Lobastov, B. Loher, Y. -G. Ma, A. Makankin, A. Maksymchyuk, A. Malakhov, I. Mardor, S. Merts, A. Morozov, S. Morozov, G. Musulmanbekov, R. Nagdasev, D. Nikitin, V. Palchik, D. Peresunko, M. Peryt, O. Petukhov, Yu. Petukhov, S. Piyadin, V. Plotnikov, G. Pokatashkin, Yu. Potrebenikov, O. Rogachevsky, V. Rogov, K. Roslon, D. Rossi, I. Rufanov, P. Rukoyatkin, M. Rumyantsev, D. Sakulin, V. Samsonov, H. Scheit, A. Schmidt, S. Sedykh, I. Selyuzhenkov, P. Senger, S. Sergeev, A. Shchipunov, A. Sheremeteva, M. Shitenkov, V. Shumikhin, A. Shutov, V. Shutov, H. Simon, I. Slepnev, V. Slepnev, I. Slepov, A. Sorin, V. Sosnovtsev, V. Spaskov, T. Starecki, A. Stavinskiy, E. Streletskaya, O. Streltsova, M. Strikhanov, N. Sukhov, D. Suvarieva, J. Tanaka, A. Taranenko, N. Tarasov, O. Tarasov, V. Tarasov, A. Terletsky, O. Teryaev, V. Tcholakov, V. Tikhomirov, A. Timoshenko, N. Topilin, B. Topko, H. Tornqvist, I. Tyapkin, V. Vasendina, A. Vishnevsky, N. Voytishin, V. Wagner, O. Warmusz, I. Yaron, V. Yurevich, N. Zamiatin, Song Zhang, E. Zherebtsova, V. Zhezher, N. Zhigareva, A. Zinchenko, E. Zubarev, M. Zuev

Measuring the microscopic structure of such systems is a formidable challenge, often met by particle knockout scattering experiments.

Nuclear Experiment Nuclear Theory

Self-Supervised Learning Aided Class-Incremental Lifelong Learning

no code implementations10 Jun 2020 Song Zhang, Gehui Shen, Jinsong Huang, Zhi-Hong Deng

Lifelong or continual learning remains to be a challenge for artificial neural network, as it is required to be both stable for preservation of old knowledge and plastic for acquisition of new knowledge.

class-incremental learning Incremental Learning +1

Generative Feature Replay with Orthogonal Weight Modification for Continual Learning

no code implementations7 May 2020 Gehui Shen, Song Zhang, Xiang Chen, Zhi-Hong Deng

For this scenario, generative replay is a promising strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting.

class-incremental learning Incremental Learning

Deep learning for smart fish farming: applications, opportunities and challenges

no code implementations6 Apr 2020 Xinting Yang, Song Zhang, Jintao Liu, Qinfeng Gao, Shuanglin Dong, Chao Zhou

This change can create new opportunities and a series of challenges for information and data processing in smart fish farming.

Decision Making

Hybrid calibration procedure for fringe projection profilometry based on stereo-vision and polynomial fitting

no code implementations9 Mar 2020 Raul Vargas, Andres G. Marrugo, Song Zhang, Lenny A. Romero

The key to accurate 3D shape measurement in Fringe Projection Profilometry (FPP) is the proper calibration of the measurement system.

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