Paper: A Unified Framework for Heterogeneous Semi-supervised Learning (ICLR-2024)

Reviewer comment (Writing):
The key problems in the heterogeneous SSL problem, and how to deal with these problems in the proposal? [See Weakness part]

Author response:
* **About presentation**

We thank the reviewer for the suggestion about the paper organization and we will adjust the paper accordingly in the final version.
This novel heterogeneous SSL task is much more challenging compared with the traditional SSL task due to the following characteristics: (1) The domain gap, expressed as the divergence between class feature distributions across the labeled and unlabeled domains, presents a significant impediment to model generalization and learning. (2) The absence of annotated samples from the unlabeled domain during training further compounds the complexity of the task. (3) Considering that the test set comprises samples from both domains, the devised solution methods need to accurately model the distributions inherent to each domain.
Traditional SSL overlooks the domain heterogeneity within both the training and testing data, whereas UDA exclusively concentrates on the unlabeled domain as the target domain. Therefore, traditional SSL and UDA methods are not readily applicable or effective in addressing the proposed HSSL task

Reviewer comment (Experiments):
How does the performance of Uni-HSSL compare to the ICML23 work? Bidirectional adaptation for robust semi-supervised learning with inconsistent data distributions. ICML'23

Author response:
* **About comparing to ICML23 work (BiAdopt)**

Our Heterogeneous Semi-supervised Learning (HSSL) setup introduces a key distinction compared to the Semi-Supervised Learning with Inconsistent Data Distributions setup:
Our proposed setup aims to train a model capable of robust performance and generalization on a test set derived from **both the labeled and unlabeled domains**. In contrast, the test set in the Semi-Supervised Learning with Inconsistent Data distribution setup is **exclusively sampled from the labeled domain**, presenting a notably less challenging problem compared to our proposed setup.

Moreover, we have incorporated comparisons with BiAdopt in Tables 1, 2, 3, and 4. The updated tables showcase the superior performance of our proposed Uni-HSSL across all datasets. Notably, our Uni-HSSL outperforms BiAdopt with performance gains surpassing 5.5%, 6.9%, 14%, and 4% on Office-31, Office-Home, VisDA and ISIC-2019 datasets, respectively. These results underscore the robustness of Uni-HSSL and highlight the limitations of BiAdopt in effectively addressing the challenges posed by the proposed HSSL task.

Reviewer comment (Experiments):
The paper only considers the DA dataset. I suggest authors could further investigate the effectiveness of their proposed framework in additional settings, such as imbalanced SSL with different imbalance ratios between labeled and unlabeled data on CIFAR10/100-LT benchmark.

Author response:
* **About using CIFAR10/100**

Heterogeneous Semi-supervised Learning (HSSL) requires the labeled and unlabeled domains to have distinct feature distributions, necessitating the condition that $P_l(\mathbf{x}|y) \neq P_u(\mathbf{x}|y)$. However, conventional datasets like CIFAR typically represent a single domain and thus don't inherently meet this condition for HSSL, which requires distinct feature distributions across domains.

Reviewer comment (Experiments):
Some robust SSL methods are not compared, such as [5][9] in the experimental setting. And the authors only compare two UDA methods. The recent SOTA UDA methods [10] are missed.

Author response:
* **About adding more comparisons**

We have incorporated the comparison with [9] into our analysis. It is important to note that the PatchMix [10] utilizes the Transformer as its underlying backbone and it is not directly applicable to change its backbone therefore direct comparison is not suitable. Due to time constraints, we were unable to conduct experiments comparing our proposed method to [5] and [10] within the allotted rebuttal period. However, we commit to including these comparisons in the final version of the paper.