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 | 50 | 6.70% |
Retrieval | 38 | 5.09% |
Semantic Segmentation | 28 | 3.75% |
Question Answering | 27 | 3.62% |
Large Language Model | 25 | 3.35% |
Object Detection | 14 | 1.88% |
Image Classification | 13 | 1.74% |
Image Segmentation | 12 | 1.61% |
Benchmarking | 12 | 1.61% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |