1344 lines
52 KiB
Plaintext
1344 lines
52 KiB
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{{hash=9dddfcfd529634a150ee38ee5c0203d7}{%
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}
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\field{labeltitlesource}{title}
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\field{booktitle}{2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}
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\field{title}{MobileNetV2: Inverted Residuals and Linear Bottlenecks}
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\field{year}{2018}
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\field{pages}{4510\bibrangedash 4520}
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|
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\range{pages}{11}
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|
|
\verb{doi}
|
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|
|
\verb 10.1109/CVPR.2018.00474
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|
|
\endverb
|
||
|
|
\keyw{Manifolds;Neural networks;Computer architecture;Standards;Computational modeling;Task analysis}
|
||
|
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\endentry
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{{hash=3b500b0dfd88e6e151d29108fdcb82f0}{%
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{{hash=4712800a228b1179529b9f6e0d1b1838}{%
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{{hash=41ad6ff6c026d5a3730269072b31caf1}{%
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{{hash=b02f7871db6fc5524cec4ce38e104410}{%
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{{hash=1dec75595b55bf77971f6a932d146b81}{%
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{{hash=b6680dbb0176cb9bd87a3b26fa6f5cfb}{%
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{{hash=04426b798803cfaf3e8aa9280a5d0a58}{%
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{{hash=fa7242e11c7d955de2ac1be94ca29073}{%
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{{hash=8c9ee8f70a3c3d97f85efd01c4e9cbe6}{%
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{{hash=8e4243c228c72a5e5279e31252887b32}{%
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family={Martin\bibnamedelima Wattenberg},
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{{hash=c6a6eb2597f23589fc9141bdda275996}{%
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family={Martin\bibnamedelima Wicke},
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familyi={M\bibinitperiod\bibinitdelim W\bibinitperiod}}}%
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{{hash=3ea39e6dc6ef47029ae996c7e63f1a48}{%
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family={Yuan\bibnamedelima Yu},
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familyi={Y\bibinitperiod\bibinitdelim Y\bibinitperiod}}}%
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{{hash=b69feb3a3d59a312b20dbef0b1d2d6de}{%
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family={Xiaoqiang\bibnamedelima Zheng},
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familyi={X\bibinitperiod\bibinitdelim Z\bibinitperiod}}}%
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}
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\strng{namehash}{7fdd865be502254047a3b2638dc0cfeb}
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\strng{fullhash}{9b332dc9b33a2f6316d71d525269bd0f}
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\strng{bibnamehash}{7fdd865be502254047a3b2638dc0cfeb}
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\strng{authorbibnamehash}{7fdd865be502254047a3b2638dc0cfeb}
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\strng{authornamehash}{7fdd865be502254047a3b2638dc0cfeb}
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\strng{authorfullhash}{9b332dc9b33a2f6316d71d525269bd0f}
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\field{labelnamesource}{author}
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|
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\field{labeltitlesource}{title}
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||
|
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\field{note}{Software available from tensorflow.org}
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||
|
|
\field{title}{{TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems}
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||
|
|
\field{year}{2015}
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||
|
|
\verb{urlraw}
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||
|
|
\verb https://www.tensorflow.org/
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|
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\endverb
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|
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\verb{url}
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||
|
|
\verb https://www.tensorflow.org/
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|
|
\endverb
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|
|
\endentry
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\entry{pytorch}{incollection}{}
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\name{author}{21}{}{%
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{{hash=56bf0b340039cf8594436a624ff548a9}{%
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family={Paszke},
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familyi={P\bibinitperiod},
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given={Adam},
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giveni={A\bibinitperiod}}}%
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{{hash=4ba5062e5919c814aceec188d54c01f2}{%
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family={Gross},
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familyi={G\bibinitperiod},
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given={Sam},
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giveni={S\bibinitperiod}}}%
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{{hash=e5dfae4582081d649e3a0d5342050016}{%
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family={Massa},
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familyi={M\bibinitperiod},
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given={Francisco},
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giveni={F\bibinitperiod}}}%
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{{hash=b5815e1692fa2d0c1f44eecf509bd7c4}{%
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family={Lerer},
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familyi={L\bibinitperiod},
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given={Adam},
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giveni={A\bibinitperiod}}}%
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{{hash=b75383e6b48c8360c7a60031424c85cf}{%
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family={Bradbury},
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familyi={B\bibinitperiod},
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given={James},
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giveni={J\bibinitperiod}}}%
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{{hash=f897ed422c34d95af2e22778dfc2607e}{%
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family={Chanan},
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familyi={C\bibinitperiod},
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given={Gregory},
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giveni={G\bibinitperiod}}}%
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{{hash=046269e070246feb6f394141db80ed87}{%
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|
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family={Killeen},
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familyi={K\bibinitperiod},
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given={Trevor},
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giveni={T\bibinitperiod}}}%
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{{hash=c40352c194e60a3ef458ee7e8685afb5}{%
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family={Lin},
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familyi={L\bibinitperiod},
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given={Zeming},
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giveni={Z\bibinitperiod}}}%
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{{hash=6e45f49ec618e619efad90c8e8a61f0c}{%
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family={Gimelshein},
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familyi={G\bibinitperiod},
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|
given={Natalia},
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|
giveni={N\bibinitperiod}}}%
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|
|
{{hash=f65a80959d520337ae99a0798515036c}{%
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|
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family={Antiga},
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|
|
familyi={A\bibinitperiod},
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||
|
|
given={Luca},
|
||
|
|
giveni={L\bibinitperiod}}}%
|
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|
|
{{hash=954cf7680b6ce14813973eccdca3c4bc}{%
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||
|
|
family={Desmaison},
|
||
|
|
familyi={D\bibinitperiod},
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||
|
|
given={Alban},
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||
|
|
giveni={A\bibinitperiod}}}%
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|
|
{{hash=c1b8f8db68d6667b9f2f9a9a3567721b}{%
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||
|
|
family={Kopf},
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||
|
|
familyi={K\bibinitperiod},
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||
|
|
given={Andreas},
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||
|
|
giveni={A\bibinitperiod}}}%
|
||
|
|
{{hash=b9e701339e56fd0b171145b08288a1b7}{%
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||
|
|
family={Yang},
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||
|
|
familyi={Y\bibinitperiod},
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||
|
|
given={Edward},
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||
|
|
giveni={E\bibinitperiod}}}%
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||
|
|
{{hash=3f9535be511fd2fa346093e63b8e61a0}{%
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||
|
|
family={DeVito},
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||
|
|
familyi={D\bibinitperiod},
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|
|
given={Zachary},
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||
|
|
giveni={Z\bibinitperiod}}}%
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||
|
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{{hash=d814afaa50b9e22ab92cc9f8f9a9e43a}{%
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||
|
|
family={Raison},
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||
|
|
familyi={R\bibinitperiod},
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||
|
|
given={Martin},
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||
|
|
giveni={M\bibinitperiod}}}%
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|
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{{hash=3feeeebee8583ecc208f7fb3e0a55068}{%
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||
|
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family={Tejani},
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familyi={T\bibinitperiod},
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given={Alykhan},
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||
|
|
giveni={A\bibinitperiod}}}%
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||
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{{hash=e18536d5cb7543731fbf2ca1a4908732}{%
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family={Chilamkurthy},
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{{hash=0a0b028c6b85c46f368317d0c5bfe3a0}{%
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family={Steiner},
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familyi={S\bibinitperiod},
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giveni={B\bibinitperiod}}}%
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{{hash=998a001f16bb57c079c1d5afb1cb02c8}{%
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family={Fang},
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familyi={F\bibinitperiod},
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giveni={L\bibinitperiod}}}%
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{{hash=3f19c633bbfb847db6a0e71d3659eacd}{%
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family={Bai},
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{{hash=8ef51a0906e47d2b4472c4e714ed598f}{%
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family={Chintala},
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familyi={C\bibinitperiod},
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\field{journaltitle}{arXiv preprint arXiv:2206.11404}
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\field{title}{The ArtBench Dataset: Benchmarking Generative Models with Artworks}
|
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\endentry
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{{hash=9f4b2bda38961146065556b1d28f38ab}{%
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{{hash=e52876f830a8a20786ff3e4d7dd6f083}{%
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given={Pietro},
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giveni={P\bibinitperiod}}}%
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{CaltechDATA}%
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\field{title}{Caltech 256}
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||
|
|
\field{year}{2022}
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||
|
|
\verb{doi}
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||
|
|
\verb 10.22002/D1.20087
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|
|
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|
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{{hash=b1fb544937854da3ec8ec4f8109e846d}{%
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{{hash=66af1ecffa9fdc06f0e4ac2c3f2e4124}{%
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given={J.},
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{{hash=912e9620e6b1bb780e26082faac6a619}{%
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family={Rahtu},
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{{hash=7b37b33590b6ced45c1ce1cddab2ef7a}{%
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given={M.},
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{{hash=0ec8712a7d032edd8ddc33c250c0784f}{%
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family={Vedaldi},
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familyi={V\bibinitperiod},
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given={A.},
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giveni={A\bibinitperiod}}}%
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}
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\strng{bibnamehash}{4929dc1922cbfb934b74d90436a4ded6}
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\strng{authorbibnamehash}{4929dc1922cbfb934b74d90436a4ded6}
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\field{labelnamesource}{author}
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||
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|
|
\field{eprintclass}{cs-cv}
|
||
|
|
\field{eprinttype}{arXiv}
|
||
|
|
\field{title}{Fine-Grained Visual Classification of Aircraft}
|
||
|
|
\field{type}{techreport}
|
||
|
|
\field{year}{2013}
|
||
|
|
\verb{eprint}
|
||
|
|
\verb 1306.5151
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||
|
|
\endverb
|
||
|
|
\endentry
|
||
|
|
\entry{fooddataset}{article}{}
|
||
|
|
\name{author}{5}{}{%
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||
|
|
{{hash=9745e23b5afda022dab01c159a454bb2}{%
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|
family={Kaur},
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||
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|
familyi={K\bibinitperiod},
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||
|
|
given={Parneet},
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||
|
|
giveni={P\bibinitperiod}}}%
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|
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{{hash=5582bf1be9db7a164fe4a89365a4420b}{%
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||
|
|
family={Sikka},
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||
|
|
familyi={S\bibinitperiod},
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|
|
given={Karan},
|
||
|
|
giveni={K\bibinitperiod}}}%
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{{hash=47ad65c82b1de7d642988df185d7d8ea}{%
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family={Wang},
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||
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|
familyi={W\bibinitperiod},
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given={Weijun},
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||
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giveni={W\bibinitperiod}}}%
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{{hash=4d59c02623535d66620842635043b3b7}{%
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|
|
family={Belongie},
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||
|
|
familyi={B\bibinitperiod},
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|
|
given={serge},
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||
|
|
giveni={s\bibinitperiod}}}%
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|
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{{hash=2478c221c08a4d32c950a414c383fb08}{%
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||
|
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family={Divakaran},
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||
|
|
familyi={D\bibinitperiod},
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|
given={Ajay},
|
||
|
|
giveni={A\bibinitperiod}}}%
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||
|
|
}
|
||
|
|
\strng{namehash}{9bbebb9e370b0e4d2c8850515561bfbd}
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||
|
|
\strng{fullhash}{ee23ed313476a626198320adcc67e276}
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\strng{bibnamehash}{ee23ed313476a626198320adcc67e276}
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\strng{authorbibnamehash}{ee23ed313476a626198320adcc67e276}
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\field{sortinit}{4}
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||
|
|
\field{labelnamesource}{author}
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||
|
|
\field{labeltitlesource}{title}
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||
|
|
\field{journaltitle}{arXiv preprint arXiv:1907.06167}
|
||
|
|
\field{title}{{FoodX-251: A Dataset for Fine-grained Food Classification}}
|
||
|
|
\field{year}{2019}
|
||
|
|
\endentry
|
||
|
|
\enddatalist
|
||
|
|
\endrefsection
|
||
|
|
\endinput
|
||
|
|
|