In [1]:
import pprint
import feedparser
In [2]:
url = 'http://arxiv.org/rss/cs.CV'
In [3]:
d = feedparser.parse(url)
In [4]:
pprint.pprint(d, depth=1)
{'bozo': 0,
'encoding': 'us-ascii',
'entries': [...],
'etag': '"Fri, 26 Jul 2019 00:30:00 GMT", "1564101000"',
'feed': {...},
'headers': {...},
'href': 'http://export.arxiv.org/rss/cs.CV',
'namespaces': {...},
'status': 301,
'updated': 'Fri, 26 Jul 2019 00:30:00 GMT',
'updated_parsed': time.struct_time(tm_year=2019, tm_mon=7, tm_mday=26, tm_hour=0, tm_min=30, tm_sec=0, tm_wday=4, tm_yday=207, tm_isdst=0),
'version': 'rss10'}
In [5]:
print(type(d['entries']))
<class 'list'>
In [6]:
print(len(d['entries']))
67
In [7]:
print(type(d['entries'][0]))
<class 'feedparser.FeedParserDict'>
In [8]:
pprint.pprint(d['entries'][0], width=100)
{'author': '<a href="http://arxiv.org/find/cs/1/au:+Kurmi_V/0/1/0/all/0/1">Vinod Kumar Kurmi</a>, '
'<a href="http://arxiv.org/find/cs/1/au:+Bajaj_V/0/1/0/all/0/1">Vipul Bajaj</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Subramanian_V/0/1/0/all/0/1">Venkatesh K '
'Subramanian</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Namboodiri_V/0/1/0/all/0/1">Vinay P '
'Namboodiri</a>',
'author_detail': {'name': '<a href="http://arxiv.org/find/cs/1/au:+Kurmi_V/0/1/0/all/0/1">Vinod '
'Kumar Kurmi</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Bajaj_V/0/1/0/all/0/1">Vipul '
'Bajaj</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Subramanian_V/0/1/0/all/0/1">Venkatesh '
'K Subramanian</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Namboodiri_V/0/1/0/all/0/1">Vinay '
'P Namboodiri</a>'},
'authors': [{'name': '<a href="http://arxiv.org/find/cs/1/au:+Kurmi_V/0/1/0/all/0/1">Vinod Kumar '
'Kurmi</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Bajaj_V/0/1/0/all/0/1">Vipul '
'Bajaj</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Subramanian_V/0/1/0/all/0/1">Venkatesh '
'K Subramanian</a>, <a '
'href="http://arxiv.org/find/cs/1/au:+Namboodiri_V/0/1/0/all/0/1">Vinay P '
'Namboodiri</a>'}],
'id': 'http://arxiv.org/abs/1907.10628',
'link': 'http://arxiv.org/abs/1907.10628',
'links': [{'href': 'http://arxiv.org/abs/1907.10628', 'rel': 'alternate', 'type': 'text/html'}],
'summary': '<p>Domain adaptation is essential to enable wide usage of deep learning based\n'
'networks trained using large labeled datasets. Adversarial learning based\n'
'techniques have shown their utility towards solving this problem using a\n'
'discriminator that ensures source and target distributions are close. However,\n'
'here we suggest that rather than using a point estimate, it would be useful if\n'
'a distribution based discriminator could be used to bridge this gap. This could\n'
'be achieved using multiple classifiers or using traditional ensemble methods.\n'
'In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator\n'
'could suffice to obtain the distribution based discriminator. Specifically, we\n'
'propose a curriculum based dropout discriminator that gradually increases the\n'
'variance of the sample based distribution and the corresponding reverse\n'
'gradients are used to align the source and target feature representations. The\n'
'detailed results and thorough ablation analysis show that our model outperforms\n'
'state-of-the-art results.\n'
'</p>',
'summary_detail': {'base': 'http://export.arxiv.org/rss/cs.CV',
'language': None,
'type': 'text/html',
'value': '<p>Domain adaptation is essential to enable wide usage of deep '
'learning based\n'
'networks trained using large labeled datasets. Adversarial learning '
'based\n'
'techniques have shown their utility towards solving this problem '
'using a\n'
'discriminator that ensures source and target distributions are '
'close. However,\n'
'here we suggest that rather than using a point estimate, it would be '
'useful if\n'
'a distribution based discriminator could be used to bridge this gap. '
'This could\n'
'be achieved using multiple classifiers or using traditional ensemble '
'methods.\n'
'In contrast, we suggest that a Monte Carlo dropout based ensemble '
'discriminator\n'
'could suffice to obtain the distribution based discriminator. '
'Specifically, we\n'
'propose a curriculum based dropout discriminator that gradually '
'increases the\n'
'variance of the sample based distribution and the corresponding '
'reverse\n'
'gradients are used to align the source and target feature '
'representations. The\n'
'detailed results and thorough ablation analysis show that our model '
'outperforms\n'
'state-of-the-art results.\n'
'</p>'},
'title': 'Curriculum based Dropout Discriminator for Domain Adaptation. (arXiv:1907.10628v1 '
'[cs.LG])',
'title_detail': {'base': 'http://export.arxiv.org/rss/cs.CV',
'language': None,
'type': 'text/plain',
'value': 'Curriculum based Dropout Discriminator for Domain Adaptation. '
'(arXiv:1907.10628v1 [cs.LG])'}}
In [9]:
print(d['entries'][0]['link'])
http://arxiv.org/abs/1907.10628
In [10]:
print(d['entries'][0]['title'])
Curriculum based Dropout Discriminator for Domain Adaptation. (arXiv:1907.10628v1 [cs.LG])
Content source: nkmk/python-snippets
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