4

There are several generative models that have been proposed before or roughly at the same time of the GAN (2014). For example, the deep Boltzman machine (2009), deep generative stochastic network (2014) or variational auto-encoder (2014).


3

The purpose of Reinforcement Learning is to maximize some notion of cumulative reward, leading me to the point (1) : as far as I understand, there is no timesteps in your problem and the "reward" is immediate. Thus, I don't think reinforcement is suitable here. On an other hand, in supervised learning, linear regression is the task of approximating a ...


2

I'm not an expert on that so you could probably get a better answer. I'm not sure to understand what you're looking for. Are the couple of images about the same thing? Like pictures of cats and you want to generate a new cat based on these pictures? If that's what you want, you could probably take a look at Generative Adversarial Network (GAN) : ...


2

The only disadvantage and difference between these generative models and the method you describe, is the input. You describe inputting tags, where as for a GAN, or VAE, the generation segment of the model takes in some representation of a probability distribution. For a GAN, it's mostly random noise, and for a VAE it is some latent space (see nbros answer). ...


1

I will only focus on the VAE because I am more familiar with it, but the explanations may also apply to the GAN and other generative models. In the case of the VAE, you train a neural network not only to generate images but to represent them compactly in a so-called latent space, so you train the VAE to do dimensionality reduction. More precisely, the VAE ...


1

I believe you may want to use a Sum Product Network for this task. SPNs are the state-of-the-art approach for face completion, and there are several more recent papers on this topic since the original above. Importantly, the SPN paper also covers other approaches that work well for this task. If lower-resolution results are acceptable for your task, PCA ...


1

The model (that I know of) which most resembles your description is the auto-encoder, which is trained to learn a compact representation (a vector) of the input, which can later be used to reconstruct the original input. In a certain way, this compact representation (implicitly) encodes the most important features of the input. In particular, you may be ...


1

As an example, assuming that you want to load a (numpy array) file called 'my_training_data.npy' that is in the default 'My Drive' folder of your Google Drive... import numpy as np from google.colab import drive drive.mount('/content/drive') path = '/content/drive/My Drive/' my_training_data = np.load(path + 'my_training_data.npy')


1

I wouldn't really consider what they are doing as AI - they are using a script that intelligently overlaps various images of existing people in order to create a new face. Animating those images isn't impossible - essentially extrapolation + additional "real" images will be used to know what the face would like from all angles and in all states (happy, sad, ...


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