Feedforward Networks
Dense Connections
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
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 44 | 6.08% |
| Retrieval | 33 | 4.56% |
| Question Answering | 28 | 3.87% |
| Large Language Model | 24 | 3.31% |
| Semantic Segmentation | 21 | 2.90% |
| Decoder | 21 | 2.90% |
| Image Classification | 15 | 2.07% |
| Classification | 14 | 1.93% |
| Sentence | 12 | 1.66% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
