Convolutional Neural Networks
FractalNet
Introduced by Larsson et al. in FractalNet: Ultra-Deep Neural Networks without ResidualsFractalNet is a type of convolutional neural network that eschews residual connections in favour of a "fractal" design. They involve repeated application of a simple expansion rule to generate deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers.
Source: FractalNet: Ultra-Deep Neural Networks without Residuals
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Recognition | 1 | 33.33% |
| Translation | 1 | 33.33% |
| Image Classification | 1 | 33.33% |
Batch Normalization
Dense Connections
Fractal Block
Max Pooling
ReLU
Softmax
