Which architecture can help address convergence issues in traditional GANs?RNNDBNWGANCNNLSTM

To address convergence issues in traditional Generative Adversarial Networks (GANs), architectures that incorporate mechanisms for sequence processing or improved stability are often considered. The options provided include RNN, DBN, WGAN, CNN, and LSTM.

1. WGAN (Wasserstein GAN): This architecture specifically targets the convergence issues of traditional GANs by utilizing the Wasserstein distance as a loss function. WGANs provide a more stable training process and better convergence characteristics compared to classic GANs.

2. LSTM (Long Short-Term Memory): While LSTM networks are primarily used for sequential data, they can contribute to modeling dependencies over time in generated sequences. However, LSTMs do not directly address the convergence issues seen in GANs.

3. RNN (Recurrent Neural Network): Similar to LSTMs, RNNs can also model sequences but suffer from the same limitations as LSTMs regarding convergence consistency in GAN frameworks.

4. DBN (Deep Belief Network): Although DBNs can be used for generative modeling, they are not specifically designed to solve GAN convergence issues.

5. CNN (Convolutional Neural Network): While CNNs are widely used for image generation and play a role in GAN architecture, they do not specifically address the convergence problem.

Final Answer

The architecture that best addresses convergence issues in traditional GANs is WGAN (Wasserstein GAN).