Target noise: A pre-training based neural network initialization for efficient high resolution learning
#Neural Networks #Weight Initialization #Self-supervised Learning #Target Noise #Convergence Efficiency #High Resolution Learning #Kaiming Initialization
📌 Key Takeaways
- Researchers have developed a new initialization method called 'Target Noise' to replace traditional random sampling techniques.
- The method uses self-supervised pre-training with random noise as the target to better prepare neural network weights.
- Traditional methods like Xavier and Kaiming fail to utilize optimization metadata, leading to potential convergence delays.
- The new strategy is specifically designed to improve efficiency and performance in high-resolution machine learning tasks.
📖 Full Retelling
A team of researchers introduced a novel neural network initialization strategy called 'Target Noise' in a technical paper published on the arXiv preprint server on February 11, 2025, to address the convergence inefficiencies associated with traditional random weight initialization methods. The study argues that conventional approaches like Xavier and Kaiming initialization are limited because they rely on purely random sampling rather than incorporating information from the optimization process. By utilizing self-supervised pre-training with random noise as a target, the researchers aim to provide a more robust starting point for high-resolution learning tasks, potentially accelerating training times and improving model performance.
The core of the proposed method lies in its departure from established industry standards that have dominated deep learning for years. While Xavier and Kaiming initializations were designed to keep signal variance stable across layers, they do not 'prepare' the network for the specific structural demands of the data it will eventually process. The 'Target Noise' approach bridges this gap by engaging the network in a preliminary phase where it learns to map inputs to a noise-based distribution. This phase acts as a warm-up, structuring the weights in a way that makes subsequent fine-tuning on actual datasets more efficient.
Preliminary findings suggest that this pre-training initialization is particularly beneficial for high-resolution learning, where the complexity of the data often leads to optimization bottlenecks. By initializing with a network that has already 'seen' a data distribution—even one consisting of random noise—the optimization landscape becomes smoother. This research contributes to a growing body of work seeking to refine the foundational architecture of artificial intelligence, shifting away from purely stochastic setups toward more intentional, data-informed starting states for neural models.
🏷️ Themes
Machine Learning, Artificial Intelligence, Optimization
📚 Related People & Topics
Neural network
Structure in biology and artificial intelligence
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks.
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📄 Original Source Content
arXiv:2602.06585v1 Announce Type: cross Abstract: Weight initialization plays a crucial role in the optimization behavior and convergence efficiency of neural networks. Most existing initialization methods, such as Xavier and Kaiming initializations, rely on random sampling and do not exploit information from the optimization process itself. We propose a simple, yet effective, initialization strategy based on self-supervised pre-training using random noise as the target. Instead of directly tra