Teacher Forcing & Back Propagation

Teacher forcing is a technique used during training in sequence prediction tasks, particularly in sequence-to-sequence models such as recurrent neural networks (RNNs) or transformers. It involves feeding the model the actual (or ground truth) target outputs from the training data at each time step during training, rather than using the model’s own predictions from the previous time step. Teacher forcing can help stabilize training and improve convergence, especially in the early stages of training.

Backpropagation is a fundamental algorithm used for training neural networks, including sequence-to-sequence models. It is a method for computing the gradient of the loss function with respect to the model’s parameters, which allows for updating the parameters in a way that minimizes the loss function. Backpropagation works by propagating the error backwards through the network, computing gradients at each layer using the chain rule of calculus.

While teacher forcing and back propagation are both used in training neural networks, they serve different purposes:

• Teacher forcing is a training technique specific to sequence prediction tasks, aimed at stabilizing and improving training convergence by providing the model with ground truth outputs during training.
• Backpropagation is a general algorithm for computing gradients and updating parameters in neural networks, allowing the model to learn from the training data by minimizing the loss function.

In many sequence-to-sequence models, teacher forcing is used during training to stabilize training, while backpropagation is used to compute gradients and update parameters based on the loss incurred by the model’s predictions compared to the ground truth outputs. Both techniques are essential components of the training process and work together to train the model effectively.