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 | 6.50% |
| Semantic Segmentation | 25 | 3.69% |
| Image Classification | 22 | 3.25% |
| Self-Supervised Learning | 21 | 3.10% |
| Object Detection | 20 | 2.95% |
| Speech Recognition | 16 | 2.36% |
| Question Answering | 15 | 2.22% |
| Sentiment Analysis | 15 | 2.22% |
| Machine Translation | 14 | 2.07% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |