deletions | additions       diff --git a/bibliography/biblio.bib b/bibliography/biblio.bib  index 9058743..44ad694 100644  --- a/bibliography/biblio.bib  +++ b/bibliography/biblio.bib 
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pages = {1097--1105},  file = {NIPS Full Text PDF:C\:\\Users\\yunfuliu\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\yx5a4mz5.default\\zotero\\storage\\5IICUFCH\\Krizhevsky 等。 - 2012 - ImageNet Classification with Deep Convolutional Ne.pdf:application/pdf;NIPS Snapshort:C\:\\Users\\yunfuliu\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\yx5a4mz5.default\\zotero\\storage\\N7HBINNB\\4824-imagenet-classification-with-deep-.html:text/html},  }  @inproceedings{zeiler_deconvolutional_2010,  title = {{Deconvolutional networks}},  abstract = {Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning framework where features that capture these mid-level cues spontaneously emerge from image data. Our approach is based on the convolutional decomposition of images under a sparsity constraint and is totally unsupervised. By building a hierarchy of such decompositions we can learn rich feature sets that are a robust image representation for both the analysis and synthesis of images. 1.},  booktitle = {In {CVPR}},  author = {Zeiler, Matthew D. and Krishnan, Dilip and Taylor, Graham W. and Fergus, Rob},  year = {2010},  file = {Citeseer - Snapshot:C\:\\Users\\yunfuliu\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\yx5a4mz5.default\\zotero\\storage\\XKZZ3ET7\\summary.html:text/html},  }