Ortogonal Inicialização de Pesos is a technique used in the training of redes neurais to set the initial weights of the model in a way that enhances performance. This method involves initializing the weights such that they are orthogonal to each other, meaning that the produto escalar of any two weight vectors equals zero. This property helps maintain the variance of activations across layers, which is crucial for effective training.
In traditional weight initialization methods, like random initialization, weights can lead to issues such as vanishing or exploding gradients, particularly in deep networks. These problems can significantly slow down training or even prevent the model from learning at all. By using inicialização ortogonal, each neuron’s output is less likely to become too small or too large as it passes through the network, thereby facilitating better flow of gradients during backpropagation.
The orthogonal weight initialization technique is particularly beneficial in architectures like Redes Neurais Recorrentes (RNNs) and deep feedforward networks, where maintaining stable gradients is essential for convergence. This method can be implemented using various libraries and frameworks that support neural network training, contributing to faster convergence rates and improved overall model performance.