Summary and Contributions: In this paper, the authors show that the quality of noising plays a crucial role in semi-supervised learning. The authors propose that using advanced data augmentation methods rather than simple noising improves performance on various text and vision related tasks. They use consistency loss to constrain the model predictions of unlabeled examples to be invariant to noise from data augmentations.
Strengths: The paper presents strong and extensive empirical results and outperform state of the art on different tasks. The paper is well written and easy to understand.
Weaknesses: The authors do not provide strong theoretical guarantees for why the method works. Its hard to establish the role the advanced data augmentation strategies play compared to the parameters/hyper-parameters used in tuning the models. The work is not novel as the various transformations applied to the data are well established. The authors apply them for semi-supervised learning so their effectiveness is not surprising.
Correctness: The empirical methodology is correct as the authors show the performance of their method of various tasks.
Clarity: The paper is clear and well written.
Relation to Prior Work: The authors have applied existing methods for a new problem and shown good results. In essence, the main difference to prior work is domain the methods are applied to.
Additional Feedback: The main comment I have regarding the paper is that the authors do not provide adequate justification as to why the advanced data augmentation work compared to the simple ones and when to apply them. - The intuition provided in the theory is that unsupervised data augmentation will traverse the entire sub-graph of each disconnected component. This same intuition can be applied for other semi-supervised methods like nearest neighbor and label propagation. These methods will assign the same labels to unlabeled data examples within its component in a graph. - Assuming the noise eps is between (0,1), theorem 1 says that the error of UDA decreases as we get more labeled examples and the error increases as the noise increases. This is intuitive but does not explain why the noise from the advanced data augmentation methods are better for semi-supervised learning or provide guarantees for when they work. ========================================= I acknowledge that I read the rebuttal and thank the authors for providing explanations to the questions and concerns I had.
Summary and Contributions: This paper introduces a generic method for semi-supervised learning to drastically improve the performance by applying strong data augmentation to unlabeled data and forcing the consistency of representation between such augmented data. The method is evaluated on both vision and language. The comprehensive experiments demonstrate the effectiveness of UDA.
Strengths: * The method does not depend on domain-specific data augmentation (e.g., mixup). Therefore, UDA can be applied to various domains, such as vision and language, as experimented in this paper. * The effectiveness of the method is comprehensively evaluated vision and language classification tasks, and UDA outperforms baselines in a large margin.
Weaknesses: * In terms of "valid noise" mentioned in L117, I think the adaptive variant of AutoAugment used in ReMixMatch [Berthelot et al. 2020] is more suitable for vision task. On the other hand, RandAugment used in UDA shares magnitude parameters, and some operations may be too strong or too weak, while others are appropriate. Berthelot et al. 2020 ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
Correctness: * Empirically, the method is well evaluated. * The theoretical analysis is overall sound, but I cannot understand what makes the following state correct: L 192, "performing unsupervised data augmentation ensures the traversal of the entire sub-graph C_i". The concern here is resolved in the rebuttal. The figures well explain the idea.
Clarity: The paper is well written and well organized.
Relation to Prior Work: The relation to prior work is well discussed. Yet, the reference is not well refined: for example, L472 Wide ResNet is accepted to BMVC, but the paper cites its arxiv version.
Summary and Contributions: They have shown that in semi-supervised learning (SSK) tasks, the use of advanced data augmentation techniques such as RandAugment and back-translation can improve the performance of the consistency based training method. Through experiments, they show that the use of the augmentation policy can boost the performance compared to other methods and show that the technique is widely useful in vision and language tasks.
Strengths: 1. The simplicity of the method is favorable aspect. Then, as we have more advanced data augmentation technique, the scheme of the method should be applicable too. 2. They show that the proposed way is widely applicable, not limited to image classification task. It will attract the attention from many researchers of wide area.
Weaknesses: 1. While its simplicity, their contribution can be limited since they simply replaced the augmentation with recent state-of-the art data augmentation presented in other papers. 2. We could see that the use of RandAugment can improve the performance of SSL. But, which augmentation technique (cropping, random distortion...) was the key of improvement, or the use of all techniques was the key? If we also combine VAT with the proposed method, how the performance will be? This kind of analysis looks lacking and the empirical insight obtained from the paper is a little limited.
Correctness: The claims and method should be correct.
Clarity: Yes, the paper is well written and very easy to follow.
Relation to Prior Work: The relation is clearly discussed and their contribution is clear.
Additional Feedback: I have a mixed thought on this paper. While great improvement on SSL task, the method does not look novel to me. If there is anything I missed in this paper, please point out it and respond to weaknesses I have shown. My concern was addressed after rebuttal and raised the score.
Summary and Contributions: In this paper, the authors propose to use advanced data augmentation techniques in supervised learning as a superior source of noise in consistency training. The authors also provide a theoretical analysis of how UDA improves the classification performance and the corresponding role of the state-of-the-art augmentation. Empirical studies on a wide variety of language and vision tasks show significant improvements over state-of-the-art models with much less data.
Strengths: 1. Overall, the paper is well written and easy to follow. 2. It is an interesting idea to utilize data augmentation techniques in supervised learning as a superior source of data for consistency training. This idea is well motivated and both empirically and theoretically validated, making the method convincing. 3. Abundant and extensive experiments are conducted and the experimental results are really promising and encouraging.
Weaknesses: The most impressive point of this paper is its really perfect empirical results. However, it looks not surprising to me that superior data augmentations can benefit SSL since weak augmentation methods such as cropping have already been adopted in consistency training. Except for the insights from theoretical analysis, the novelty of the method seems to be limited since most of the techniques used are well established.
Correctness: The claims and methods are both theoretically and empirically validated.
Clarity: Yes. The paper is well formed and easy to follow.
Relation to Prior Work: Clear.