Successive Refinement with Decoder Cooperation and its Channel Coding Duals

We study cooperation in multi terminal source coding models involving successive refinement. Specifically, we study the case of a single encoder and two decoders, where the encoder provides a common description to both the decoders and a private description to only one of the decoders. The decoders cooperate via cribbing, i.e., the decoder with access only to the common description is allowed to observe, in addition, a deterministic function of the reconstruction symbols produced by the other. We characterize the fundamental performance limits in the respective settings of non-causal, strictly-causal and causal cribbing. We use a new coding scheme, referred to as Forward Encoding and Block Markov Decoding, which is a variant of one recently used by Cuff and Zhao for coordination via implicit communication. Finally, we use the insight gained to introduce and solve some dual channel coding scenarios involving Multiple Access Channels with cribbing.Comment: 55 pages, 15 figures, 8 tables, submitted to IEEE Transactions on Information Theory. A shorter version submitted to ISIT 201

Capacity of a POST Channel with and without Feedback

We consider finite state channels where the state of the channel is its previous output. We refer to these as POST (Previous Output is the STate) channels. We first focus on POST($\alpha$) channels. These channels have binary inputs and outputs, where the state determines if the channel behaves as a $Z$ or an $S$ channel, both with parameter $\alpha$. %with parameter $\alpha.$ We show that the non feedback capacity of the POST($\alpha$) channel equals its feedback capacity, despite the memory of the channel. The proof of this surprising result is based on showing that the induced output distribution, when maximizing the directed information in the presence of feedback, can also be achieved by an input distribution that does not utilize of the feedback. We show that this is a sufficient condition for the feedback capacity to equal the non feedback capacity for any finite state channel. We show that the result carries over from the POST($\alpha$) channel to a binary POST channel where the previous output determines whether the current channel will be binary with parameters $(a,b)$ or $(b,a)$. Finally, we show that, in general, feedback may increase the capacity of a POST channel

ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the GAN latent space. In this paper, we propose ClusterGAN as a new mechanism for clustering using GANs. By sampling latent variables from a mixture of one-hot encoded variables and continuous latent variables, coupled with an inverse network (which projects the data to the latent space) trained jointly with a clustering specific loss, we are able to achieve clustering in the latent space. Our results show a remarkable phenomenon that GANs can preserve latent space interpolation across categories, even though the discriminator is never exposed to such vectors. We compare our results with various clustering baselines and demonstrate superior performance on both synthetic and real datasets.Comment: GANs, Clustering, Latent Space, Interpolation (v2 : Typos fixed, some new experiments added, reported metrics on best validated model.