Adaptive Prototypical Networks

Prototypical network for Few shot learning tries to learn an embedding function in the encoder that embeds images with similar features close to one another in the embedding space. However, in this process, the support set samples for a task are embedded independently of one other, and hence, the inter-class closeness is not taken into account. Thus, in the presence of similar-looking classes in a task, the embeddings will tend to be close to each other in the embedding space and even possibly overlap in some regions, which is not desirable for classification. In this paper, we propose an approach that intuitively pushes the embeddings of each of the classes away from the others in the meta-testing phase, thereby grouping them closely based on the distinct class labels rather than only the similarity of spatial features. This is achieved by training the encoder network for classification using the support set samples and labels of the new task. Extensive experiments conducted on benchmark data sets show improvements in meta-testing accuracy when compared with Prototypical Networks and also other standard few-shot learning models

Client Side Channel State Information Estimation for MIMO Communication

Multiple-input multiple-output (MIMO) system relies on a feedback signal which holds channel state information (CSI) from receiver to the transmitter to do pre-coding for achieving better performance. However, sending CSI feedback at each time stamp for long duration is an overhead in the communication system. We introduce a deep reinforcement learning based channel estimation at receiver end for single user MIMO communication without CSI feedback. In this paper we propose to train the receiver with known pilot signals to analyse the stochastic behaviour of the wireless channel. The simulation on MIMO channel with additive white Gaussian noise (AWGN) shows that our proposed method can learn the different characteristics affecting the channel with limited number of pilot signals. Extensive experiments show that the proposed method was able to outperform the existing state-of-the-art end to end reinforcement learning method. The results demonstrate that the proposed method learns and predicts the stochastic time varying channel characteristic accurately at receiver’s end