Initialization
Kaiming Initialization
Introduced by He et al. in Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationKaiming Initialization, or He Initialization, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as ReLU activations.
A proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:
$$\frac{1}{2}n_{l}\text{Var}\left[w_{l}\right] = 1 $$
This implies an initialization scheme of:
$$ w_{l} \sim \mathcal{N}\left(0, 2/n_{l}\right)$$
That is, a zero-centered Gaussian with standard deviation of $\sqrt{2/{n}_{l}}$ (variance shown in equation above). Biases are initialized at $0$.
Source: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 47 | 7.37% |
| Self-Supervised Learning | 40 | 6.27% |
| Semantic Segmentation | 31 | 4.86% |
| Classification | 24 | 3.76% |
| Image Segmentation | 15 | 2.35% |
| Object Detection | 13 | 2.04% |
| Quantization | 11 | 1.72% |
| Decoder | 9 | 1.41% |
| Denoising | 8 | 1.25% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
