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[bibtex]@InProceedings{Tiwari_2023_ICCV, author = {Tiwari, Rishabh and Chavan, Arnav and Gupta, Deepak and Mago, Gowreesh and Gupta, Animesh and Gupta, Akash and Sharan, Suraj and Yang, Yukun and Zhao, Shanwei and Wang, Shihao and Kwak, Youngjun and Jeong, Seonghun and Lee, Yunseung and Kim, Changick and Kim, Subin and Gankhuyag, Ganzorig and Jung, Ho and Ryu, Junwhan and Kim, HaeMoon and Kim, Byeong H. and Vo, Tu and Zaheer, Sheir and Holston, Alexander and Park, Chan and Dixit, Dheemant and Lele, Nahush and Bhushan, Kushagra and Bhowmick, Debjani and Arya, Devanshu and Gulshad, Sadaf and Habibian, Amirhossein and Ghodrati, Amir and Bejnordi, Babak and Gupta, Jai and Liu, Zhuang and Yu, Jiahui and Prasad, Dilip and Shen, Zhiqiang}, title = {RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2023}, pages = {1534-1543} }
RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning
Abstract
This paper delves into the results of two resource-constrained deep learning challenges, part of the workshop on Resource-Efficient Deep Learning for Computer Vision (RCV) at ICCV 2023, focusing on memory and time limitations. The challenges garnered significant global participation and showcased a range of intriguing solutions. The paper outlines the problem statements for both tracks, summarizes baseline and top-performing approaches, and provides a detailed analysis of the methods used. While the presented solutions constitute promising initial progress, they represent the beginning of efforts needed to address this complex issue. We conclude by emphasizing the importance of sustained research efforts to fully address the challenges of resource-constrained deep learning.
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