First thank you Alexander and Ava for sharing the knowledge After watching these videos, I realized that learning machine learning is not just a skill; teaching is a much bigger skill.
I am curious, regarding the CycleGANs with respect to audio generation, would the output from the model be better if the person creating the input audio were to try and mimic the person the model was trained on as closely as possible? For example, if an Obama impersonator were to supply the input audio, would the output even more closely resemble that of Obama's true voice? The same question would also apply to the video content. If body-language were more closely mimicking the target, does the model generate an output that more closely resembles the target? My hunch is that it would indeed improve the prediction.
I guess its because once the training is done and as the neural network weights are fixed , as there is no backpropogation etcc.., involved after training , the weights couldn't change and thus for every input you would get the same output as learnt function doesnt involve any probabilistic element.
I have a dataset of 120 images of cell phone photographs of the skin of dogs sick with 12 types of skin diseases, with a distribution of 10 images for each dog. What type of Generative Adversarial Network (GAN) is most suitable to increase my dataset with quality and be able to train my DL model? DcGAN, ACGAN, StyleGAN3, CGAN?
