Weight clipping in WGANs can lead to several issues:
1. Vanishing Gradients: When the clipping window is too large, it can lead to vanishing gradients, which can cause the model to fail to converge or train slowly[1][2][4].
2. Slow Convergence: When the clipping window is too small, it can lead to slow convergence, which can cause the model to take a long time to train[1][2][4].
3. Pathological Value Surfaces: Weight clipping can result in pathological value surfaces for the critic, which can lead to suboptimal performance[2].
4. Capacity Underuse: Implementing a k-Lipshitz constraint via weight clipping biases the critic towards much simpler functions, which can lead to underuse of the model's capacity[2].
5. Unstable Training: Weight clipping can lead to unstable training, especially when the model is deep or has a large number of layers[1][2][4].
6. Difficulty in Training: Weight clipping can make it difficult to train the model, especially when the model is complex or has many layers[1][2][4].
7. Lack of Robustness: Weight clipping can make the model less robust to changes in the data distribution or the model architecture[1][2][4].
These issues can be mitigated by using alternative methods such as gradient penalty, which is more effective in enforcing the Lipschitz constraint and improving the training stability of WGANs[1][2][3][4].
Citations:
[1] https://www.cs.toronto.edu/~bonner/courses/2022s/csc2547/papers/adversarial/background/improved-training-of-WGANs,-gulrajani,-nips2017.pdf
[2] https://proceedings.neurips.cc/paper_files/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf
[3] https://ceur-ws.org/Vol-2563/aics_34.pdf
[4] https://blog.paperspace.com/wgans/
[5] https://stackoverflow.com/questions/58723838/if-we-can-clip-gradient-in-wgan-why-bother-with-wgan-gp
how does gradient penalty compare to weight clipping in terms of training stability
The gradient penalty in Wasserstein GANs (WGANs) offers several advantages over weight clipping in terms of training stability:
1. Capacity Underuse: Weight clipping can lead to capacity underuse, where the critic learns very simple functions due to the hard constraint on the Lipschitz constant. The gradient penalty, on the other hand, allows the critic to learn more complex functions while still satisfying the Lipschitz constraint.
2. Exploding and Vanishing Gradients: The interaction between the weight constraint and the loss function in weight clipping can lead to exploding or vanishing gradients, making the training process unstable. The gradient penalty does not suffer from this issue.
3. Pathological Value Surfaces: Weight clipping can result in pathological value surfaces for the critic, which can lead to suboptimal performance. The gradient penalty does not exhibit this behavior.
4. Robustness: The gradient penalty is more robust to architectural choices and hyperparameter tuning compared to weight clipping, making it easier to train WGANs.
5. Improved Convergence: The gradient penalty can lead to faster convergence and better sample quality compared to weight clipping, as shown in the original WGAN-GP paper.
In summary, the gradient penalty provides a more stable and effective way to enforce the Lipschitz constraint in WGANs, leading to improved training stability, convergence, and sample quality compared to weight clipping. The gradient penalty addresses the key issues associated with weight clipping, making it the preferred method for training WGANs.
Citations:[1] https://www.cs.toronto.edu/~bonner/courses/2022s/csc2547/papers/adversarial/background/improved-training-of-WGANs,-gulrajani,-nips2017.pdf
[2] https://proceedings.neurips.cc/paper_files/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf
[3] https://towardsdatascience.com/demystified-wasserstein-gan-with-gradient-penalty-ba5e9b905ead
[4] https://stackoverflow.com/questions/58723838/if-we-can-clip-gradient-in-wgan-why-bother-with-wgan-gp
[5] https://datascience.stackexchange.com/questions/31077/wgan-is-too-slow-what-are-some-ways-to-tweak-for-speed-ups