Effects of Initialization Biases on Deep Neural Network Training Dynamics
NeutralArtificial Intelligence
- Recent research highlights the phenomenon of Initial Guessing Bias in untrained large neural networks, which leads to a skewed probability distribution favoring a limited number of classes immediately after random initialization. This bias significantly influences the early training dynamics, particularly when the model is adapting to the data's coarse structure. The choice of loss function, such as Blurry and Piecewise-zero loss, plays a crucial role in how these dynamics unfold.
- Understanding the effects of initialization biases is vital for improving the training efficiency and accuracy of deep neural networks. The findings underscore the importance of selecting appropriate loss functions to mitigate the adverse impacts of Initial Guessing Bias, which can hinder the model's ability to learn effectively from the data. This research could inform future developments in neural network training methodologies.
- The exploration of biases in neural network training aligns with ongoing discussions in the field regarding the robustness of models against label errors and the effectiveness of various loss functions. As researchers continue to innovate with frameworks like Active Negative Loss and benchmarks for probabilistic robustness, the interplay between initialization biases and training strategies remains a critical area of focus in advancing machine learning techniques.
— via World Pulse Now AI Editorial System
