Title: The Power of  Semantic Similarity based Soft-Labeling for Generalized Zero-Shot Learning. Abstract: Zero-Shot Learning (ZSL) is a classification task where some classes referred as unseen classes have no labeled training images. Instead, we only have side information (or description) about seen and unseen classes, often in the form of semantic or descriptive attributes. Lack of training images from a set of classes restricts the use of standard classification techniques and losses, including the popular cross-entropy loss. The key step in tackling ZSL problem is bridging visual to semantic space via learning a nonlinear embedding. A well established approach is to obtain the semantic representation of the visual information and perform classification in the semantic space. In this paper, we propose a novel architecture of casting ZSL as a fully connected neural-network with cross-entropy loss to embed visual space to semantic space. During training in order to introduce unseen visual information to the network, we utilize soft-labeling based on semantic similarities between seen and unseen classes. To the best of our knowledge, such similarity based soft-labeling is not explored for cross-modal transfer and ZSL. We evaluate the proposed model on five benchmark datasets for zero-shot learning, AwA1, AwA2, aPY, SUN and CUB datasets, and show that, despite the simplicity, our approach achieves the state-of-the-art performance in Generalized-ZSL setting on all of these datasets and outperforms the state-of-the-art for some datasets.