How does the contrastive divergence loss function work in the training step of the EBM model?

The contrastive divergence loss function in the training of an Energy-Based Model (EBM) works by calculating the difference between the scores of fake and real observations. The real images are slightly perturbed with noise, and fake images are sampled from a buffer. The loss function includes a regularization term to prevent the scores from becoming too large. Gradients of this loss function are used to update the model's weights through backpropagation.

In the training step of an Energy-Based Model (EBM) using contrastive divergence, the model first perturbs real images with a small amount of noise to prevent overfitting. Fake images are sampled from a buffer, which includes both newly generated random noise and previously sampled images. The model computes scores for both real and fake images. The contrastive divergence loss is the difference between the average scores of fake and real images. Additionally, a regularization loss is added to the total loss to keep the scores from becoming excessively large. This combined loss is used to compute gradients, which are then applied to update the model's weights via backpropagation. This process helps the model learn to distinguish between real and fake images more effectively over time.