Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n_{\text{inputs}}*n_{\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.
$$h_{l} = g\left(\textbf{W}^{T}h_{l-1}\right)$$
where $g$ is an activation function.
Image Source: Deep Learning by Goodfellow, Bengio and Courville
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Language Modelling | 59 | 7.78% |
Image Classification | 25 | 3.30% |
Semantic Segmentation | 25 | 3.30% |
Question Answering | 22 | 2.90% |
Text Generation | 21 | 2.77% |
Object Detection | 21 | 2.77% |
Self-Supervised Learning | 19 | 2.51% |
reinforcement Learning | 15 | 1.98% |
Machine Translation | 14 | 1.85% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |