Conclusion

• CNN revolutionized the image classification field thanks to smart architecture choices that allowed them to exploit the spacial relations of images • The success of GANs as image generators builds upon the success of CNNs. This architecture allowed to train a generator exploiting the loss function of the (CNN) discriminator • Transformer used complex architecture tricks in order to encode the sentence structure while using a non sequential algorithm (at least for the training)