Orthogonal Subspace Decomposition: A New Perspective of Learning Discriminative Features for Face Clustering
Authors: Jianfeng Wang
Thomas Lukasiewicz
zhongchao shi
Venue:  (2021)

Abstract: Face  clustering  is  an  important  task,  due  to  its  wide  applications  in  practice. Graph-based  face  clustering  methods  have  recently  made  a  great  progress  and achieved new state-of-the-art results. Learning discriminative node features is the key to further improve the performance of graph-based face clustering.  To this end, we propose subspace learning as a new way to learn discriminative node features, which is implemented by a new orthogonal subspace decomposition (OSD) module. In graph-based face clustering, OSD leads to more discriminative node features,  which better reflect the relationship between each pair of faces, thereby boosting the accuracy of face clustering.  Extensive experiments show that OSD outperforms state-of-the-art results with a healthy margin.

Under review as a conference paper at ICLR 2021
ORTHOGONAL SUBSPACE DECOMPOSITION: A NEW PERSPECTIVE OF LEARNING DISCRIMINATIVE FEA- TURES FOR FACE CLUSTERING
Anonymous authors Paper under double-blind blind review
INTRODUCTION


Reviews:
[{"review_id": "W-KysCpU47J", "reviewer": "AnonReviewer5", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes an orthogonal subspace decomposition (OSD) method for the face clustering task. The OSD module can learn more discriminative node features by combining subspace learning and feature selection. A space reconstruction (SR) loss and cross-entropy loss are joint as the loss function. Experiments on datasets demonstrate the good performance of the proposed method.\nHowever, there are still several issues that should be addressed.\n1. The novelty is my major concern. The graph-based clustering and subspace learning are well-explored fields. Many works have been developed. The authors should highlight the motivations and the contributions of the proposed method that are different from other methods.\n2. It said that “We propose orthogonal subspace decomposition (OSD), which ...combine subspace learning and feature selection with neural networks”. \n1) What do the neural networks refer to? Is it the GCN-M network? Please give detailed information.\n2) In this paper, the authors didn’t introduce the baseline GCN-M. Please give a brief description of the role of GCN-M in this method.\n3) What is the effect of the proposed OSD module when replacing the benchmark GCN-M with other networks?\n3. The authors propose the SR loss for the OSD module. In Table 2, we can observe the performance decline when removing any subterm of the SR loss. However, the influence of different combinations of these loss subterms is not analyzed sufficiently. In particular, how about the performance of OSD-LP(GCN-M) without L_{SR}?\n4. The description of the LP module is not intuitive enough. It will be better to provide the framework of the LP module.\n5. Maybe authors will present an estimation of running time compared with state-of-the-art methods.", "extra_scores": {}, "extra_text": {}}, {"review_id": "6w6DnJkDS2V", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "For graph face clustering purpose, this paper proposed subspace learning as a new way to learn discriminative graph node features, which is implemented by a new orthogonal subspace decomposition (OSD) module. OSD aims at more discriminative node features, better reflecting the relationship between each pair of face. Extensive experiments show that OSD outperforms state-of-the-art results on IJB-B and VoxCeleb2 face datasets. \n\nIt is good to see this paper made an attempt on leveraging subspace learning as a feature selection tool into GCN. And the proposed space reconstruction (SR) loss seems good for improving the accuracy. \n\nI have the following comments/concerns. \n\n1. It is unclear how much computational cost was added due to the introduction of OSD module in training stage, as the authors have claimed no computational cost for the inference because the two subspace matrices are no longer needed. \n2. According to Equation 6 and 7, the total loss actually has 5 terms, which are equally weighted in this study. Is this reasonable? Of course, it is good to have no free hyper-parameters to tune. However, it seems no reason for equal weights of these different terms. So, it can be good to either have some theoretical proof for equal weights or add some empirical evaluations to explore better possibilities. \n3. For the evaluation, is the number of clusters preset or automatically learned by the algorithm? If it was not preset (but auto-learned), how F1 and NMI take into account the possible error in this number? If it was preset, it is good to clearly explain in the paper and discuss how the method can be extended in this direction. \n", "extra_scores": {}, "extra_text": {}}, {"review_id": "65DRjPJmLA9", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes an orthogonal subspace decomposition (OSD) module, which helps the deep network learn more discriminative node features in graph-based face clustering. The experimental results indicate the superiority of OSD compared with other types of loss functions.\n\n - Strengths:\n     1. The proposed OSD module is grounded in mathematical. \n     2. The OSD module requires a light additional computation cost, and it is easy to implement.\n     3. The comparison with SOTA and the ablation study prove the effectiveness of the four components of the OSD module.\n  \n - Weakness:\n     1. The proposed OSD is only an auxiliary module under the LP(GCN-M) framework, which seems with insufficient value for an ICLR paper. Could this module be applied to other frameworks like contrastive learning? More experiments could be conducted to study the generality of the module.\n     2. Why are the best results in Table 2 inconsistent with those in Table 1? Does it due to the different experimental settings?\n     3. The first ablation study seems to have little relevancy with the proposed module, but is a study of hyper-parameters of LP(GCN-M). It could be replaced by other ablation studies which provide a deeper understanding of the OSD module.\n    4. why specifically consider face clustering? is it impossible to employ your method to handle other data? e.g., imagenet, cifar100, stl, etc.\n     \n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: During the discussion phase, although the reviewers acknowledge the effectiveness of the proposed approach, they raised the concern about the novelty of the paper.

In my opinion, I also agree that the novelty is not well justified in this paper. In the related work section, although the authors put an effort to review the existing studies of subspace learning and feature selection, their relationship (similarity and/or difference) to the proposed method is not discussed. Since the idea of using subspace learning and feature selection in clustering is standard, the novelty of this work should be introduction of the integration step into neural networks, which is not significant enough in its current state. The paper becomes more significant if, for example, theoretically discuss the unique characteristics of the integration into NNs which does not appear in the usual setting. 

In addition, the motivation of face clustering is not convincing. I recommend either (1) use and discuss the domain specific property of the problem of face clustering in the proposed method, or (2) construct a general clustering method. Since the authors present additional experiments in the author response, I guess (2) fits. Then, however, the paper should be re-organized.

The readability can be improved. For example, Algorithm 1 receives training data {X, A}, but I cannot find the definition of A. Also, please italicize mathematical symbols.

Overall, the paper is still not ready for publication, I will therefore reject the paper.

Decision: Reject