Generalized zero-shot learning (GZSL) is the task of predicting a test image from seen or unseen classes using pre-defined class-attributes and images from the seen classes. Typical ZSL models assign the class corresponding to the most relevant attribute as the predicted label of the test image based on the learned relation between the attribute and the image. However, this relation-based approach presents a difficulty: many of the test images are predicted as biased to the seen domain, i.e., the domain bias problem. Recently, many methods have addressed this difficulty using a synthesis-based approach that, however, requires generation of large amounts of high-quality unseen images after training and the additional training of classifier given them. Therefore, for this study, we aim at alleviating this difficulty in the manner of the relation-based approach. First, we consider the requirements for good performance in a ZSL setting and introduce a new model based on a variational autoencoder that learns to embed attributes and images into the shared representation space which satisfies those requirements. Next, we assume that the domain bias problem in GZSL derives from a situation in which embedding of the unseen domain overlaps that of the seen one. We introduce a discriminator that distinguishes domains in a shared space and learns jointly with the above embedding model to prevent this situation. After training, we can obtain prior knowledge from the discriminator of which domain is more likely to be embedded anywhere in the shared space. We propose combination of this knowledge and the relationbased classification on the embedded shared space as a mixture model to compensate class prediction. Experimentally obtained results confirm that the proposed method significantly improves the domain bias problem in relation-based settings and achieves almost equal accuracy to that of high-cost synthesis-based methods.
Summary:
This paper proposes a relation-based ZSL model which can effectively alleviate the domain bias problem. To this end, first, the paper claims that a good relation-based ZSL model should consider two requirements -- modality invariance and class separability. And the paper designed Modality-invariant and Class-separable Multimodal VAE (MCMVAE) based on VAEs to meet the two aforementioned requirements. Next, the paper hypothesizes that the domain bias problem is due to the overlap between seen and unseen classes in the shared space, and explicitly introduced a discriminator to separate the two domains. The paper performs experiments on ZSL benchmark datasets and shows that the proposed method outperforms other relation-based methods. Besides, the domain discriminator which can be applied to other models demonstrates its effectiveness in reducing domain bias given the experimental results.
+Strengths:
1. Clear writing logic. The author clearly depicts how to get the final loss of the method step-to-step and the relationship with existing methods.
2. The version without the domain discriminator (i.e. MCMVAE) is similar to PSE and CADA-VAE as the author acknowledges. However, the domain discriminator has certain novelty and can be applied to other methods. The overlap among seen and unseen classes is an important problem (domain bias problem named by the author) and the add of the domain discriminator to distinguish whether a sample is from seen classes or unseen classes is reasonable, which can provide better class separability (among seen and unseen classes).
-Weaknesses:
1. Although the author claims that the proposed method is a relation-based method, it is strange that the proposed method is called xxVAE but in Table 2 it doesn't fall into synthesis-based methods (as CVAE-ZSL and CADA-VAE do). Although it is derived from VAE, the current method doesn't seem to be called a VAE any more (some of the regularizations of the VAE are relaxed). Also, are the two terms -- relation-based and synthesis-based -- first proposed by the author? Is there a clear boundary between those two groups of methods?
2. It is recommended that an additional figure that depicts the framework is added (similar to Figure 2 in CADA-VAE) to promote better understanding. Currently, the method part only contains formulas with many parameters, making it difficult to grasp the idea of the whole framework at first glance.
3. The novelty of this paper is somewhat limited while missing some relevant works, e.g.[r1, r2]. [r1] learns a latent space where the compactness within class and separateness between classes are considered. [r2] uses a two-stage prediction for GZSL.
[r1] Jiang et al. Learning Discriminative Latent Attributes for Zero-Shot Classification. In IEEE ICCV 2017.
[r2] Zhang et al. Model Selection for Generalized Zero-shot Learning. In arXiv 2018.
4. It is a question whether the seen and unseen classes can be separated (Whether a two stage process is correct?). The key for ZSL is knowledge transfer and the base is that seen and unseen classes are related [r3]. If they are separated, can one use the model trained on seen classes to recognize the unseen classes? This is quite problematic. Besides, in Tab.2 there lacks of necessary comparisons with recent relation-based approaches e.g.[r3][r4], which makes the evaluation less sufficient.
[r3] Jiang et al. Transferable Contrastive Network for Generalized Zero-Shot Learning. In IEEE ICCV 2019.
[r4] Li et al. Discriminative Learning of Latent Features For Zero-Shot Recognition. In IEEE CVPR 2018.
5. Some unclear/incorrect descriptions of the method:
5.1) The formulation of GZSL is incorrect. Y= union(y_s  y_u), but not intersection(y_s  y_u)
5.2) How is the class separation formulated in the framework?
5.3) In Sec.3.2, why is the log-likelihood of the generative models can be obtained by the L1 loss?
Minor issues:
1. Better use vectorgraphs for clear view (especially for Figure 3 and 4). 
2. Incomplete reference: for Probabilistic semantic embedding (PSE), the reference should add the conference information.
3. Grammar and spelling mistakes: 
[1] Content in Figure 2 (not caption): unseen class -> unseen classes
[2] Last line in 4.1: MCVAE-D -> MCMVAE-D
[3] Last paragraph in 4.2: close -> stay close
[4] Last model name in Table 1: MCMVAE -> MCMVAE-D
4. The color bar for the contours at the rightmost of Figure 3 is not clear (not the standard way to draw a color bar, better refer to what a color bar is usually drawn).
5. If possible, better reduce the main text to 8 pages as recommended by the submission instructions (e.g. some content of the method part can be moved to the appendix?).