The proposed adversarial autoencoder framework can be extended to handle more complex data modalities beyond images by adapting the network architecture and loss functions to suit the characteristics of text or audio data. For text data, the input representation can be encoded using techniques like word embeddings or transformer models. The encoder-decoder structure of the autoencoder can be modified to handle sequential data, with the decoder generating text sequences. The adversarial component can be designed to ensure that the latent space captures the distribution of text data effectively.
Similarly, for audio data, the input can be transformed into spectrograms or other suitable representations for processing by the network. The encoder-decoder architecture can be adjusted to handle the temporal nature of audio signals, with the decoder generating audio waveforms. The adversarial training can focus on aligning the latent space distribution with the characteristics of audio features.
In both cases, the key lies in designing the network components to capture the unique properties of the data modality while maintaining the adversarial training framework to enhance the generative capabilities of the model.