In generative models, semi-supervised learning can be applied by leveraging both labeled and unlabeled data to improve performance, especially when labeled datasets are small. Here are the steps you can follow:
- Generative Adversarial Networks (GANs): Use a semi-supervised GAN (SGAN) where the discriminator is trained to classify both real and generated samples, as well as to predict labels for labeled data.
- Labeled Loss + Unlabeled Loss: Combine a supervised loss (using labeled data) and an unsupervised loss (using unlabeled data) to train the generator.
- Pseudo-Labeling: Use the generator to create pseudo-labels for the unlabeled data, which helps the model learn from previously unlabeled samples.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Semi-Supervised GAN (SGAN): The Discriminator is tasked with both distinguishing real from fake and classifying labeled data.
- Supervised Loss: The discriminator learns from both labeled and unlabeled data using a classification loss on labeled data.
- Unsupervised Loss: The generator learns to create realistic samples even from unlabeled data, contributing to the overall training.
- Pseudo-Labeling: The generator's outputs can be used to assign pseudo-labels to unlabeled data, further enhancing learning.
Hence, by referring to the above, you can apply semi-supervised learning in generative models to improve performance with small labeled datasets.
