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 | 53 | 8.01% |
Self-Supervised Learning | 48 | 7.25% |
Semantic Segmentation | 34 | 5.14% |
Classification | 33 | 4.98% |
Object Detection | 14 | 2.11% |
Image Segmentation | 13 | 1.96% |
Quantization | 11 | 1.66% |
Decoder | 10 | 1.51% |
Speaker Verification | 7 | 1.06% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |