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unmixed    音标拼音: [ənm'ɪkst]
a. 未混杂他物的,纯粹的

未混杂他物的,纯粹的

unmixed
adj 1: not mixed with extraneous elements; "plain water"; "sheer
wine"; "not an unmixed blessing" [synonym: {plain}, {sheer},
{unmingled}, {unmixed}]
2: not constituting a compound [synonym: {uncompounded}, {unmixed}]

Unmixed \Unmixed\
See {mixed}.


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英文字典中文字典相关资料:


  • Masked Autoencoders Are Scalable Vision Learners
    In CVPR, 2021 [9] Xinlei Chen, Saining Xie, and Kaiming He An empirical study of training self-supervised Vision Transformers In ICCV, 2021 [10] Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning ELECTRA: Pre-training text encoders as discriminators rather than generators In ICLR, 2020 [11] Corinna Cortes and Vladimir
  • [2111. 06377] Masked Autoencoders Are Scalable Vision Learners
    View a PDF of the paper titled Masked Autoencoders Are Scalable Vision Learners, by Kaiming He and 5 other authors
  • Kaiming He - Publications
    Masked Autoencoders Are Scalable Vision Learners Kaiming He*, Xinlei Chen *, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick Computer Vision and Pattern Recognition (CVPR), 2022 (Oral)
  • ‪Kaiming He‬ - ‪Google Scholar‬
    ‪Associate Professor, EECS, MIT‬ - ‪‪Cited by 833,743‬‬ - ‪Computer Vision‬ - ‪Machine Learning‬
  • Masked Autoencoders Are Scalable Vision Learners | IEEE . . .
    This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels It is based on two core designs First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens
  • Masked Autoencoders Are Scalable Vision Learners - ResearchGate
    Download Citation | On Jun 1, 2022, Kaiming He and others published Masked Autoencoders Are Scalable Vision Learners | Find, read and cite all the research you need on ResearchGate
  • Masked Autoencoders Are Scalable Vision Learners - Computer
    2022,pp 15979-15988, DOI Bookmark: 10 1109 CVPR52688 2022 01553 Keywords Training, Couplings, Computer Vision, Computational Modeling, Computer Architecture, Data Models, Pattern Recognition, Authors Kaiming He , Facebook AI Research (FAIR) Xinlei Chen , Facebook AI Research (FAIR) Saining Xie , Facebook AI Research (FAIR) Yanghao Li , Facebook AI Research (FAIR) Piotr Dollar , Facebook AI
  • dblp: Masked Autoencoders Are Scalable Vision Learners.
    Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross B Girshick: Masked Autoencoders Are Scalable Vision Learners CVPR 2022: 15979-15988
  • GitHub Pages - Yanghao Li
    Paper Code Masked Autoencoders are Scalable Vision Learners Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick Computer Vision and Pattern Recognition (CVPR), 2022 (Oral) Best Paper Nominee Paper Code MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
  • Masked Autoencoders Are Scalable Vision Learners
    Masked Autoencoders As Spatiotemporal Learners Christoph Feichtenhofer Haoqi Fan Yanghao Li Kaiming He Computer Science, Physics NeurIPS 2022





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