Conclusion

The project is now finished ! I would like to conclude this blog by summarizing the results and my experience : We started by simple models for naive  generation, trying to understand which architecture would generate sharper images. Of course we could not expect to come out with interesting results without using the captions, but… Continue reading Conclusion

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Conditional LSGAN

Since my first attempts weren't very satisfying, I decided to move forward to a more sophisticated and better performing model : Least-Squares GAN. Generative Adversarial Networks have already showed their performance on image generation. Specifically, DCGANs showed impressive performance in unsupervised learning for image generation. The architecture of a traditional GAN is the following : A… Continue reading Conditional LSGAN

Convolutional Auto-Encoder

First Model My first attempt for this class project is to implement a Convolutional Auto-Encoder, which a modified form of the following architecture. The specific activation functions I used (Tanh and Elu) result from various tests I did over small train set sizes. Exponential linear unit is useful for dropping out useless information while keeping […]