Kaiming 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 ClassificationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Classification | 57 | 9.05% |
Self-Supervised Learning | 44 | 6.98% |
Semantic Segmentation | 33 | 5.24% |
Classification | 29 | 4.60% |
Object Detection | 13 | 2.06% |
Quantization | 12 | 1.90% |
Image Segmentation | 11 | 1.75% |
Denoising | 8 | 1.27% |
Network Pruning | 7 | 1.11% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |