Group Norm for Learning Structured SVMs with Unstructured Latent Variables
Latent variables models have been applied to a number of computer vision problems. However, the complexity of the latent space is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to over fitting and are slower to perform infe...
Main Authors: | , , |
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Other Authors: | |
Format: | Article |
Language: | en_US |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2015
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Online Access: | http://hdl.handle.net/1721.1/100043 https://orcid.org/0000-0002-2231-7995 |