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About Deep Boltzmann Machine Paper
The document describes a Deep Boltzmann Machine (DBM) model for learning from multimodal data consisting of different input modalities like images and text. The model learns a joint probability distribution over the multimodal inputs to extract a unified representation. This representation can be used for classification and information retrieval tasks, and can also fill in missing modalities. Experimental results on image-text data show the model learns a good generative model that outperforms other methods like SVMs and LDA on classification, and can do tasks like image annotation and retrieval by sampling from the conditional distributions.
Detailed Information
| Author: | ['sagnik ghosal'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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