Please provide your Coverage Python code calculation. Otherwise, we do not know what we suppose to optimize for.
Indeed, the concept behind a Conditional Variational Autoencoder (CVAE) as a Generative Neural Network is to generate diverse and distinctive outputs by incorporating random factors, known as “latent space” factors, into the generation process. And it is the key advantage allowed the generation of diverse samples even when conditioned on the same input.
But, it can also introduce challenges when it comes to reproducing results. Even if you use the same parameters and input conditions, the random sampling during the generation process can lead to different results each time. That’s why you could observe the scores vary.
There are some methods to add some level of determinism to the CVAE’s generation process. However, it’s important to note that enforcing too much determinism can limit the model’s ability to produce diverse and novel outputs. It is advisable to strike an appropriate balance between randomness and determinism.
Thank you for your question.
The calculation of coverage Python code is an integral part of the Scoring Algorithm, and unfortunately, it cannot be shared.
To optimize your solution, we recommend focusing on the following idea: aim to generate x0 or x1 vectors that are as diverse as possible. In other words, strive to encompass the entire range of values within the interval [0,1] to the fullest extent possible. Tips: think how many points [0,1] were covered by x0 samples generated for one specific y.
