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+The new framework of Generative Adversial Nets is aimed towards
+replicating the probability distribution of the training data (and still
+the generated data is very different from original training data). The
+framework involves training two models simultaneously: a generative
+model G and a discriminative model D. The generative model G captures
+the probability distribution of training data and tries to replicate it.
+The discriminative model D outputs a probability (0-1) that the given
+data is from the training data rather than G. The training procedure for
+G is such that it propels D towards making a mistake. It is like a
+minimax two player game. Competition in this game drives both the models
+to improve themselves until generated data can't be differentiated from
+the original data. Back propagation is sufficient for this model and
+there is no need of Markov chains, thus it is computationally efficient.
+We define input noise p(z) and map it to G (z, Θ), where Θ is parameter
+of generative model G. Similarly, we also define a second model D (x,
+Θ). We train D to maximise its probability of detecting training data.
+Simultaneously, we train G to minimise log(1-D(G(z))). In this way,
+two-player minimax game is played with value function V (G, D): V (G, D)
+= E [log D(x)] + E [log(1-D(G(z)))] //E-expectation Optimizing D in the
+inner loop is computationally expensive, so we implement k steps of
+minibatch stochastic gradient descent of D and one step of G (momentum
+was used in original research, however we can use any other gradient
+descent optimizer). The training objective for D can be interpreted as
+maximizing the log-likelihood for estimation for conditional probability
+P(Y = y|x), where Y indicates whether x comes from training data(with y
+= 1) or from generative model(with y = 0). We prove that the algorithm
+converges with probability distribution of generative model being equal
+to the probability distribution of original training data and output of
+D becoming ½ for all by using Kullback-Leibler divergence and
+Jenson-Shannon divergence. The model was trained on a range of datasets
+including MNIST, the Toronto Face Database and CIFAR-10. ReLU and
+sigmoid activations was used for generator nets while discriminator nets
+used maxout activations alongwith dropout for regularization. We
+estimate performance of our model using Gaussian Parzen window and it
+proved at par(if not better) with other generative models. There are
+certain advantages and disadvantages of adversial nets compared to other
+generative frameworks. Major disadvantage is that D must be synchronized
+well with G to avoid "the Helvetica scenario" in which generative model
+produces data very similar to that of training data (which can be done
+by many other computationally cheap methods). Major advantage is that
+Markov chains are never used, only backprop is used to obtain gradients
+which makes the algorithm computationally efficient. Other advantage is
+that input training data is not directly fed to generative model rather
+gradients from discriminator are used to update the generative model.
+This gives statistical advantage and ensures that the generated data is
+not directly copied from training data but is very different from
+training data while maintaining the similar probability distribution.
+(despite theoretical guarantees, the practical model is not perfect.
+Nonetheless, the performance of the model in practice suggests that they
+are a reasonable model.) \*\*\*\*\*\*SUMMARY ENDS\*\*\*\*\*\*\* P.S. –
+Please try to upload the papers at least 3-4 days before discussion. For
+a beginner in deep learning like me, it is very difficult to understand
+a lot of things and have to go through reference papers and internet to
+understand the paper clearly (but can't do because of the lack of time).
+Since it is open for all, I feel that there can be more beginners like
+me. I hope this will be sorted. Thanks.