Paper: Conditional MAE: An Empirical Study of Multiple Masking in Masked Autoencoder (ICLR-2024)

Reviewer comment (Novelty):
The contribution is limited, as the proposed method is more of an empirical technique. Although the authors have performed extensive tuning experiments, most of them are not particularly significant or are already well-known to the community (e.g., the one-shot masking).

Author response:
Firstly, we sincerely thank the reviewer for acknowledging the effort we have made to conduct extensive experiments.

**However, we do not agree with the reviewer's comment that the contribution is limited.** In this paper, we are the first to present an in-depth analysis of masking which is important for masked autoencoder. Our study gives a comprehensive empirical analysis and sheds light on how multiple masking affects the optimization in training and performance of pretrained models. More importantly, we observe that multiple masking is capable of introducing locality bias to models. e.g., introducing more locality to models. **Our contributions are also acknowledged by Reviewer JZAj and GAuN.**

**We also do not agree with the reviewer that the performance is not that significant.** In our experiments, our two-shot masking has the potential to significantly improve the baseline (MAE). Please see Figure 3(b). For example, Our two-shot masking L(0, 10) basically outperforms the baseline by 2% in ImageNet100. We also empirically demonstrate that multiple masking has the potential in fine-grained classification, outperforming baseline (MAE) in three widely-used fine-grained datasets by above 2%. Please see Table 2. Additionally, our best-performing setting with ViT-B has outperformed baseline by 0.3% in ImageNet1k. The reviewer comments that the three-shot masking is **significantly** worse than the baseline as shown in Table 5. As shown in Table 5, our three-shot masking is about 0.6% lower than the baseline. **If the 0.6% performance gap is significant, how about our enhancement of two-shot masking over baseline, which is over 2%?**

Reviewer comment (Evaluation):
The three-shot masking performs significantly worse than the baseline, as shown in Table 5, which further implies its limitation.

Author response:
The reviewer comments that the three-shot masking is **significantly** worse than the baseline as shown in Table 5. As shown in Table 5, our three-shot masking is about 0.6% lower than the baseline. **If the 0.6% performance gap is significant, how about our enhancement of two-shot masking over baseline, which is over 2%?**

We acknowledge that the three-shot masking is worse than the baseline. However, the reviewer's comment diverges from the original goal of our three-shot masking experiments. **To give a comprehensive analysis to subsequent researchers, it is our responsibility to explore the boundary of improvement, hoping to inspire future work** Additionally, though three-shot masking is worse than the baseline in the general classification, we find that the three-shot model outperforms the baseline in three fine-grained datasets by around 0.5% in Table 9 in Appendix. These results demonstrate that the three-shot masking is also meaningful.

Reviewer comment (Writing):
Readers are confronted with a perplexing dilemma due to the lack of support for motivation in the given context. The second paragraph in the introduction is particularly confusing. The reasons behind the failure of these methods and the evidence supporting the interior robustness of MAE are uncertain. The original MAE paper indicates a more comprehensive evaluation of robustness. These statements appear to be fabricated.

Author response:
**A1: We do not agree with the reviewer.** In the second paragraph, we do not mention the robustness of MAE and other methods. **How did the reviewer obtain the content about "robustness" out of thin air in the second paragraph?**

In fact, the logic of our motivation is below: Firstly, we emphasize the importance of masking operation. Then we describe the fact how other methods including MAE perform masking and select a suitable mask ratio. Afterward, in light of that masking is an important and flexible operation that can be performed at different stages, we present our concern and pose a question about a different masking strategy, i.e., multiple masking, and ask how it affects the optimization in training and performance.

**It is also acknowledged by Reviewer GAuN and JZAj that our article presents a well-organized and well-argued thought process with clear logic and structure.**

We feel sorry that the reviewer is confused. We have further revised the second paragraph carefully with better clarity as below:

- A crucial component of the masked autoencoder is the mask ratio, which directly impacts the model's performance. For instance, in MAE, the performance gap for fine-tuning accuracy may vary by up to 2\% with different mask ratios.
  However, current methods, including MAE, mostly ablate the mask ratio only on the input image: they mask the input image with various ratios and select the best-performing ratio after training those model variants. Considering that masking is an important and flexible operation that can be performed at different stages (\eg, the input image and different levels of representations) and with different ratios, these approaches may fail to fully exploit the potential of the autoencoder. Hence, a question naturally raises: **Can the masked autoencoder handle multiple rounds of masking at different levels, and how does multiple masking affect its optimization in training and performance?**

Reviewer comment (Novelty):
In fact, VideoMAE v2 is the first to propose to mask MAE. Therefore, this paper is not the first research to explore the impact of multiple shots masking in MAE.

Author response:
**A2:** By carefully going through VideoMAE v2, especially section 4.2 (Main result, Results on dual masking, https://arxiv.org/pdf/2303.16727.pdf), we agree that VideoMAE v2 is the first to propose to mask MAE. **But VideoMAE v2 is not the first work to dive deep into the impact of multiple shots masking in MAE.** We list three discrepancies between VideoMAE v2 and our work:

**Firstly, the application is different.** As the name suggested, VideoMAE v2 is designed for videos while our work is for images.

**Secondly, the motivation is different.** VideoMAE v2 is to improve the overall efficiency of computation and memory so as to perform scaling in model and data. As one can see in Table 2 Page 7 of VideoMAE v2, it primarily wants to tell the story about the benefits in computational cost, memory consumption, etc. However, the in-depth analysis of potential impact of dual masking on encoder is absent in VideoMAE v2. In contrast, our work dives deep into it and reveals what multiple shots of masking bring to the encoder and shows how it affects the encoder's optimization in training. In other words, VideoMAE v2 focuses on application while our work focuses more on analysis. This is the biggest difference between VideoMAE v2 and our work.

**Thirdly, in terms of specific implementation, the masking place and times are different.** VideoMAE v2 only performs two times of masking and employs them on encoder and decoder respectively. Differing from VideoMAE v2, our method involves three times of masking that are all performed on the encoder.

Finally, we express profound respect for all the contributors to VideoMAE v2, recognizing their efforts in scaling models and datasets within the video domain. Simultaneously, we hope that the reviewer can objectively assess the distinctions between our work and VideoMAE v2 and realize the contribution we have made in the in-depth analysis of multiple masking.

We also thank the reviewer for pointing out this problem. To avoid misunderstanding, we have cited VideoMAE v2, added the discussion in related work, and revised our description of our contribution as follows to distinguish with VideoMAE v2:

- Building on our proposed flexible framework, i.e., Conditional MAE, we are the first to make an in-depth analysis of multiple masking and reveal its impact on masked autoencoder's optimization in training and performance.

Reviewer comment (Experiments):
The experimental setting of this paper does not convince me of its results. The dataset selection has several issues. For the main experiments, the author selected the less commonly used benchmark, ImageNet-100. Since the scale and diversity of the data can significantly influence the experimental results, the validity of comparing with MAE is uncertain. Furthermore, the author also failed to choose common datasets for the downstream tasks. Moreover, the article makes adjustments under the original experimental setup conditions of MAE, thus rendering the conclusions drawn from the experiments inaccurate.

Author response:
**A3: We do not agree with the reviewer's such biased and ridiculous comments.**

Firstly, the subset of ImageNet1k, i.e., ImageNet-100, is widely used in analysis in papers [1] [2] [3] [4] [5] [6] [7] [8] published in top conferences and Journal, e.g, ICLR, NeurIPS, ICML, JMLR, etc. Besides, we also conduct experiments on large ImageNet1k to verify the effectiveness and scalability. Please see Section 3.4.

Secondly, our selected datasets for downstream tasks are widely used in the field of self-supervised learning. For example, we choose ADE20K for semantic segmentation, MSCOCO for object detection. The two datasets are also used in MAE[9], BEiT[10], CAE[11], iBOT[12], and other methods published in top conferences and Journal, e.g., CVPR, ICLR, IJCV, etc. For transfer learning, we use CIFAR100 and CIFAR10 that are also widely used in iBOT[12], MoCo v3[13], DINO[14], SimCLR[15].

Finally, parameter tuning is common and reasonable in the field of deep learning because different methods may have different fitting abilities and optimization spaces. By parameter tuning, we can find the best configuration and unleash model capabilities better. For example, iBOT [12] and DINO [14], the code of iBOT is based on DINO while in ViT-B, iBOT sets local crop scale to (0.05, 0.32) and DINO uses (0.05, 0.25) for local crop scale. Both of them perform parameter tuning for better performance.

Reviewer comment (Novelty):
The proposed method appears to lack technical contributions, and I fail to discern any tangible practical benefits in terms of performance or speed.

Author response:
**A4:** As we have mentioned in the last paragraph of the Introduction, we are not to propose a state-of-the-art method, but to enhance both the understanding and performance of MAE by exploring the potential of masking and to inspire future research. And our study actually has two aspects of contribution:

From the aspect of understanding MAE, our study shows that multiple masking introduces more locality to models, and summarizes several takeaways from our findings. Please see our Introduction. **Reviewer GAuN and JZAj also acknowledge our findings and contribution.**

From the aspect of enhancing the performance of MAE, we also empirically demonstrate that multiple masking has the potential to further improve the performance of MAE as shown in Table 6 and Figure 8. More importantly, we find multiple masking has significant superiority over MAE on fine-grained classification, which is missing in previous studies.

[1] Whitening for Self-Supervised Representation Learning. ICML2021

[2] Adversarial Masking for Self-Supervised Learning. ICML2022

[3] Mosaic Representation Learning for Self-supervised Visual Pre-training. ICLR 2023 Spotlight

[4] Self-Supervised Learning with an Information Maximization Criterion. NeurIPS 2022.

[5] solo-learn: A Library of Self-supervised Methods for Visual Representation Learning. JMLR2022

[6] Improving Transferability of Representations via Augmentation-Aware Self-Supervision. NeurIPS2021

[7] A Simple Data Mixing Prior for Improving Self-Supervised Learning. CVPR 2022.

[8] DLME: Deep Local-flatness Manifold Embedding ECCV2022

[9] Masked Autoencoders Are Scalable Vision Learners CVPR2023

[10] BEiT: BERT Pre-Training of Image Transformers. ICLR2022 Oral

[11] Context Autoencoder for Self-Supervised Representation Learning. IJCV2023

[12] iBOT: Image BERT Pre-Training with Online Tokenizer. ICLR2022

[13] An Empirical Study of Training Self-Supervised Vision Transformers. ICCV 2021

[14] Emerging Properties in Self-Supervised Vision Transformers. ICCV 2023

[15] A Simple Framework for Contrastive Learning of Visual Representations. ICML2020

Reviewer comment (Novelty):
The essence of this article is based on MAE and explores the mask strategy. However, this setting lacks novelty as there have been many studies on mask strategies (e.g., [1-3]), and the related work mentioned is not sufficient. It is hoped that the author can conduct further comparisons and supplements to evaluate performance.

Author response:
**A1:** We partially agree with the reviewer's comments that there have been many studies on mask strategies but our work is still meaningful. Because these studies primarily focus on **studying how to further improve the performance** while as we mentioned in the last paragraph of the Introduction, our work is to **reveal how multiple masking affects masked autoencoder's behavior, e.g., introducing more locality, and further enhance the understanding of masked autoencoder**. For example, [1] leverages self-attention mechanism to hide the most discriminative part and highlight the informative region to improve the accuracy of weakly supervised object Localization (WSOL). [2] uses an adversarial objective to consistently improve on state-of-the-art self-supervised learning (SSL) methods. [3] uses Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, as reconstruction target,  and verifies its effectiveness on video recognition.

We thank the reviewer's suggestion and have cited and discussed them in our related work section to show the discrepancy.

Reviewer comment (Evaluation):
The article proposes that the second-stage mask introduces more locality. However, I am concerned that the feature-level mask may have caused this result, as the input image is low-level, while the feature is high-level. Therefore, the combination of the two naturally makes SSL focus on global and local representations.

Author response:
**A2:** Thank you for the comment, but we cannot fully agree with the comment. We may need to remind the reviewer that the reconstruction target In MAE is normalized pixel value in RGB space. Even though we perform the feature-level masking, the model still needs to focus on the recovery of image pixels. Consequently, the discrepancy of the masking type is not the essence of the introduced locality. In fact, the locality is introduced because the presence of the second masking necessitates that **patches that interacted in previous layers must recover their corresponding masked neighbors in the forward pass**. As a result, the model needs to dedicate a portion of its capacity to learning how to infer local neighbors.

We have added this discussion to our paper to better clarify how the locality is introduced.

Reviewer comment (Presentation):
There are some minor changes that need to be noted. For example, the sentence "The dashed line denotes the one-shot baseline with masking ratios of 0.75 and 0.9 respectively" is unclear in terms of the baseline used in the graph. Additionally, the experiment with a mask ratio of 0.9 is missing, and if it is included in the appendix, it should be mentioned to improve readability. In the "Potential Application" section, "fine-grained" should be used instead of "fine-grind", and there are inconsistencies in the use of nouns throughout the article.

Author response:
**A4:** We feel sorry for this confusion. We have highlighted the dashed line, gone through the whole article, and revised similar issues of inconsistencies. Thank the reviewer for the kind suggestion.

Reviewer comment (Evaluation):
In Figure 4, it can be seen that the difference between layer 0-3 and layer 0-6 is significant, and according to your statement, there should be an improvement. However, the experimental results show a decrease from 84.1 to 84.0. On the other hand, the improvement from layer0-6 to layer0-9 is significant, but the graph shows that the difference from the baseline is not as significant. This has caused some confusion, and it would be helpful if the author could explain this discrepancy.

Author response:
**A5:** We sincerely thank the reviewer for this constructive suggestion. This is easy to explain. First of all, we may need to emphasize that the disparity in the heatmap does not necessarily imply whether the learned representation is advantageous or detrimental. It only reflects **how the representation learned by our two-shot masking model varies from that of the baseline.** Hence, it would be unreasonable to use the significance of heatmap to assess the performance after fine-tuning.
For L0-6, we think it is an awkward transitional state. It doesn't resemble L0-3, which learns a significantly superior discriminative ability (2.2% better than L0-6) as the initial state for fine-tuning. It also differs from L0-9 and L0-10, which exhibit stronger fitting capabilities to the data. As a result, L0-6 is relatively inferior to others. As shown in Figure 18 in Appendix, we compare the attention distance of two-shot model variants L(0;3/6/9/10/11;0.75,0.1), the results indicate that the adjustment of L0-6 is relatively inconspicuous.

We thank the reviewer again and have added this discussion to our paper to avoid confusion.

Reviewer comment (Novelty):
Originality: progressive masking has been extensively studied in generative modeling or the combination of SSL and generative modeling [1-3]; adaptive masking strategies on images or languages have also been proposed [4-5]. However, the authors did not cite or discuss them.

Author response:
We thank the reviewer for this kind suggestion. The discussion is below:

Chang et.al. introduce MaskGIT[1], which employs a bidirectional transformer decoder and is capable of learning to predict randomly masked tokens via attending to tokens in all directions during training. When inference, MaskGIT first generates all tokens of an image and then refines the generated image iteratively based on the previous generation. Recently, Chang et.al. propose Muse[2] and train it to predict randomly masked image tokens given the text embedding extracted from a pre-trained large language model (LLM). Leveraging LLM enables Muse to understand fine-grained language, translate to high-fidelity image generation, etc. Moreover, Muse directly enables inpainting, outpainting, and mask-free editing without the need to fine-tune or invert the model. Li et al. [3] propose to use semantic tokens learned by a vector-quantized GAN at inputs and outputs and combine this with masking to unify representation learning and image generation. Bandara et al. propose an adaptive masking strategy called AdaMAE [4]. AdaMAE samples visible tokens based on the semantic context using an auxiliary sampling network and empirically demonstrates the efficacy. Xiao et al. introduce a simple yet effective adaptive masking over masking strategy called AMOM [5] to enhance the refinement capability of the decoder and make the encoder optimization easier.

We have cited these works in our paper and added this discussion in our related work section. We sincerely hope to obtain support from the reviewer.