Xavier Initialization
Definition
Xavier initialization adjusts initial model weights according to the size of the respective network layer.
A weight initialization whose variance is adapted to the input and output dimensions of a layer.
Simply explained
Before learning begins, the connections receive appropriate starting values. Their magnitude depends on the inputs and outputs of the layer.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
An MLP starts with Xavier for tanh layers and compares learning curves reproducibly.
Limits and pitfalls
- The assumptions do not fit every activation; for ReLU, He initialization is often more suitable.
Origin & history
Glorot and Bengio published the initialization in 2010 to stabilize activations and gradients.
Use in mid-sized companies
- Possible application: An MLP starts with Xavier for tanh layers and compares learning curves reproducibly.
Frequently asked questions
What does Xavier initialization mean?
A weight initialization whose variance is adapted to the input and output dimensions of a layer. What should be considered? The assumptions do not fit every activation; for ReLU, He initialization is often more suitable.
Sources
- Understanding the difficulty of training deep feedforward neural networks · Glorot and Bengio, Understanding the difficulty of training deep feedforward neural networks
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
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