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Guest editorial: Deep learning for multimedia computing

Qi, Guo-Jun.; Larochelle, Hugo; Huet, Benoit; Luo, Jiebo; Yu, Kai

IEEE Transactions on Multimedia, Vol 17, N°11, November 2015

The twenty papers in this special section aim at providing a forum to present recent advancements in deep learning research that directly concerns the multimedia community. Specifically, deep learning has successfully designed algorithms that can build deep nonlinear representations to mimic how the brain perceives and understands multimodal information, ranging from low-level signals like images and audios, to high-level semantic data like natural language. For multimedia research, it is especially important to develop deep networks to capture the dependencies between different genres of data, building joint deep representation for diverse modalities.

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Titre:Guest editorial: Deep learning for multimedia computing
Type:Journal
Langue:English
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Date:
Département:Data Science
Eurecom ref:4728
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Bibtex: @article{EURECOM+4728, doi = {http://dx.doi.org/10.1109/TMM.2015.2485538}, year = {2015}, month = {11}, title = {{G}uest editorial: {D}eep learning for multimedia computing}, author = { {Q}i, {G}uo-{J}un. and {L}arochelle, {H}ugo and {H}uet, {B}enoit and {L}uo, {J}iebo and {Y}u, {K}ai}, journal = {{IEEE} {T}ransactions on {M}ultimedia, {V}ol 17, {N}°11, {N}ovember 2015}, url = {http://www.eurecom.fr/publication/4728} }
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