Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: 
The main topic of this paper is generalized zero-shot learning. This paper modifies traditional VAE method with attribute matching prior to release the hidden features from original regularization. This paper also proposes a domain discriminator to enhance class-separability of learned features to avoid unseen classes to be covered by seen classes. Experiment results show their efficiency under relation-based setting.

Pros:
1.This paper proposes an important insight that in generalized ZSL, the unseen classes may be dominated by seen classes in the feature space.
2.An easy but efficient domain discriminator method is proposed to separate different classes to avoid domination. 
3.Even without large synthetic learning architecture, the proposed method gets comparable results.

Comments:
1.The proposed MCMVAE is No-longer a VAE but an AE with attribute matching loss. Except that a new theory of MCMVAE is proposed, it is not rigorous to relate MCMVAE to VAE.
2.Add results using synthetic architecture to get a better result will make this method more reliable.
3.Why discriminator is harmful for PSE method?

review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: 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?).
review_title: Official Blind Review #3

final_score: rating: 6: Weak Accept
review_content: The paper presents a novel approach for (generalized) Zero-shot learning (GZSL). As showing in the numerical experiments on some real data, the method demonstrates the significant improvement on the accuracy of prediction comparing to some state-of-the-art methods. 
The main key of the method is using Variational Inference, variational autoencoders. The authors have taken into account the modality of the data through reparametrize the distributions, especially the inside class invariant modality and class separability. Moreover, the authors also propose to take into account a kind of biasness domain into the learning procedure, which details in adding a regularization of the domain discriminator into the objective function.

The paper is nicely written, espcially with a clear formal introduction to the problem of GZSL.

However, I have some questions:
1) Does the test set has some labels? How do you know your method works well? I can not find where you have defined a kind of loss so that we can compare the predicted labels \hat{y}_j ?  (In Section 2.)
2) How do you learn the "replaced prior" in equation (4) ?
3) It is not enough detail on how do you optimize the objective (8) ? a detail explain algorithm would make the paper significant, indeed.
4) In Table1, would MCMVAE in the last row be MCMVAE-D ?

Final, I expect the authors will make their codes available for the readers.
review_title: Official Blind Review #1

Meta-review: This paper proposes a relation-based model that extends VAE to explicitly alleviate the domain bias problem between seen and unseen classes in the setting of generalized zero-shot learning.

Reviewers and AC think that the studied problem is interesting, the reported experimental results are strong, and the writing is clear, but the proposed model and its scientific reasoning for convincing why the proposed method is valuable is somewhat limited. Thus the authors are encouraged to further improve in these directions. In particular:

- The idea of using a variant of the widely-used domain discriminator to make seen and unseen classes distinguishable is somewhat contradicted to the basic principle of zero-shot learning. How to trade off the balance between seen and unseen classes has been an important problem in generalized ZSL. These problems need further elaboration.

- The proposed model itself is not a real "VAE", making the value of an extensive derivation based on variational inference less prominent. 

- There is also the need to compare with the baselines mentioned by the reviewers. 

Overall, this is a borderline paper. Since the above concerns were not addressed convincingly in the rebuttal, I am leaning towards rejection.
Decision: Reject