user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Power of  Semantic Similarity based Soft-Labeling for Generalized Zero-Shot Learning

Paper content:
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.
Supervised classifiers, specifically Deep Neural Networks, need a large number of labeled samples to perform well.
Deep learning frameworks are known to have limitations in fine-grained classification regime and detecting object categories with no labeled data Socher et al., 2013; Zhang & Koniusz, 2018) .
On the contrary, humans can recognize new classes using their previous knowledge.
This power is due to the ability of humans to transfer their prior knowledge to recognize new objects (Fu & Sigal, 2016; Lake et al., 2015) .
Zero-shot learning aims to achieve this human-like capability for learning algorithms, which naturally reduces the burden of labeling.
In zero-shot learning problem, there are no training samples available for a set of classes, referred to as unseen classes.
Instead, semantic information (in the form of visual attributes or textual features) is available for unseen classes (Lampert et al., 2009; 2014) .
Besides, we have standard supervised training data for a different set of classes, referred to as seen classes along with the semantic information of seen classes.
The key to solving zero-shot learning problem is to leverage trained classifier on seen classes to predict unseen classes by transferring knowledge analogous to humans.
Early variants of ZSL assume that during inference, samples are only from unseen classes.
Recent observations Scheirer et al., 2013; realize that such an assumption is not realistic.
Generalized ZSL (GZSL) addresses this concern and considers a more practical variant.
In GZSL there is no restriction on seen and unseen classes during inference.
We are required to discriminate between all the classes.
Clearly, GZSL is more challenging because the trained classifier is generally biased toward seen classes.
In order to create a bridge between visual space and semantic attribute space, some methods utilize embedding techniques (Palatucci et al., 2009; Romera-Paredes & Torr, 2015; Socher et al., 2013; Bucher et al., 2016; Xu et al., 2017; Zhang et al., 2017; Simonyan & Zisserman, 2014; Xian et al., 2016; Zhang & Saligrama, 2016; Al-Halah et al., 2016; Zhang & Shi, 2019; Atzmon & Chechik, 2019) and the others use semantic similarity between seen and unseen classes (Zhang & Saligrama, 2015; Mensink et al., 2014) .
Semantic similarity based models represent each unseen class as a mixture of seen classes.
While the embedding based models follow three various directions; mapping visual space to semantic space (Palatucci et al., 2009; Romera-Paredes & Torr, 2015; Socher et al., 2013; Bucher et al., 2016; Xu et al., 2017; Socher et al., 2013) , mapping semantic space to the visual space (Zhang et al., 2017; Shojaee & Baghshah, 2016; Ye & Guo, 2017) , and finding a latent space then mapping both visual and semantic space into the joint embedding space Simonyan & Zisserman, 2014; Xian et al., 2016; Zhang & Saligrama, 2016; Al-Halah et al., 2016) .
The loss functions in embedding based models have training samples only from the seen classes.
For unseen classes, we do not have any samples.
It is not difficult to see that this lack of training samples biases the learning process towards seen classes only.
One of the recently proposed techniques to address this issue is augmenting the loss function with some unsupervised regularization such as entropy minimization over the unseen classes .
Another recent methodology which follows a different perspective is deploying Generative Adversarial Network (GAN) to generate synthetic samples for unseen classes by utilizing their attribute information Zhu et al., 2018; Xian et al., 2018) .
Although generative models boost the results significantly, it is difficult to train these models.
Furthermore, the training requires generation of large number of samples followed by training on a much larger augmented data which hurts their scalability.
The two most recent state-of-the-art GZSL methods, CRnet (Zhang & Shi, 2019) and COSMO (Atzmon & Chechik, 2019) , both employ a complex mixture of experts approach.
CRnet is based on k-means clustering with an expert module on each cluster (seen class) to map semantic space to visual space.
The output of experts (cooperation modules) are integrated and finally sent to a complex loss (relation module) to make a decision.
CRnet is a multi-module (multi-network) method that needs end-to-end training with many hyperparameters.
Also COSMO is a complex gating model with three modules: a seen/unseen classifier and two expert classifiers over seen and unseen classes.
Both of these methods have many modules, and hence, several hyperparameters; architectural, and learning decisions.
A complex pipeline is susceptible to errors, for example, CRnet uses k-means clustering for training and determining the number of experts and a weak clustering will lead to bad results.
Our Contribution: We propose a simple fully connected neural network architecture with unified (both seen and unseen classes together) cross-entropy loss along with soft-labeling.
Soft-labeling is the key novelty of our approach which enables the training data from the seen classes to also train the unseen class.
We directly use attribute similarity information between the correct seen class and the unseen classes to create a soft unseen label for each training data.
As a result of soft labeling, training instances for seen classes also serve as soft training instance for the unseen class without increasing the training corpus.
This soft labeling leads to implicit supervision for the unseen classes that eliminates the need for any unsupervised regularization such as entropy loss in .
Soft-labeling along with crossentropy loss enables a simple MLP network to tackle GZSL problem.
Our proposed model, which we call Soft-labeled ZSL (SZSL), is simple (unlike GANs) and efficient (unlike visual-semantic pairwise embedding models) approach which achieves the state-of-the-art performance in Generalized-ZSL setting on all five ZSL benchmark datasets and outperforms the state-of-the-art for some of them.
We proposed a discriminative GZSL classifier with visual-to-semantic mapping and cross-entropy loss.
During training, while SZSL is trained on a seen class, it simultaneously learns similar unseen classes through soft labels based on semantic class attributes.
We deploy similarity based soft labeling on unseen classes that allows us to learn both seen and unseen signatures simultaneously via a simple architecture.
Our proposed soft-labeling strategy along with cross-entropy loss leads to a novel regularization via generalized similarity-based weighted cross-entropy loss that can successfully tackle GZSL problem.
Soft-labeling offers a trade-off between seen and unseen accuracies and provides the capability to adjust these accuracies based on the particular application.
We achieve state-of-the-art performance, in GZSL setting, on all five ZSL benchmark datasets while keeping the model simple, efficient and easy to train.

assistant: text: How to use cross-entropy loss for zero shot learning with soft labeling on unseen classes : a simple and effective solution that achieves state-of-the-art performance on five ZSL benchmark datasets.