Multi-modal learning can be leveraged in GANs to generate text and images together by using shared latent space and cross-modal conditioning. You can follow the following key strategies given below:
- Shared Latent Space: Use a unified latent space where both text and image features are embedded, allowing the model to learn correlations between them.
- Cross-Modal Conditioning: Condition the generator on both text and image features, enabling the generation of images that align with the given text description or vice versa.
- Text Encoder: Use a pre-trained language model (e.g., Transformer) to encode the text into a vector representation.
- Image Decoder: Use a convolutional network (e.g., DCGAN) to decode the generated image.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Multi-modal Input: Combines text embeddings (from a language model) and random noise to generate images, making the model sensitive to both modalities.
- Cross-Modal Conditioning: The generator and discriminator condition on both text and image features, ensuring that the generated images are consistent with the provided text.
- Latent Space Fusion: Merges noise and text embeddings in a shared latent space to create meaningful representations.
- Adversarial Training: Utilizes adversarial loss to improve the quality of the generated images and ensure alignment with the provided text.
Hence, by referring to the above, you can leverage multi-modal learning to improve GAN output when generating text and images together.
