A Deep Belief Network (DBN) is a multi-layer generative graphical model. DBNs have bi-directional connections (RBM-type connections) on the top layer while the bottom layers only have top-down connections. They are trained using layerwise pre-training. Pre-training occurs by training the network component by component bottom up: treating the first two layers as an RBM and training, then treating the second layer and third layer as another RBM and training for those parameters.
Source: Origins of Deep Learning
Image Source: Wikipedia
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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General Classification | 18 | 14.75% |
Classification | 10 | 8.20% |
Image Classification | 9 | 7.38% |
Time Series Analysis | 5 | 4.10% |
Dimensionality Reduction | 5 | 4.10% |
Denoising | 4 | 3.28% |
Speech Recognition | 4 | 3.28% |
Object Recognition | 4 | 3.28% |
Image Segmentation | 3 | 2.46% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |