Paper: Information Retention via Learning Supplemental Features (ICLR-2024)

Reviewer comment (Evaluation):
One of my biggest concern is: in the abstract, the motivation was to enable better ood/distribution shift prediction. but none of the evaluation reflects this. in fact, the motivation seem to focus on low-resource settings. Does the authors focus is to generalize from learning on a small sample? If yes, this should be made clearer in the writing.

Author response:
**Weakness  1**: We believe that our proposed InfoR-LSF has the ability to generalize outside the domain since InfoR-LSF is wedded to learn more versatile relevant features. To verify this hypothesis, we choose the sentiment classification task, and use the full-size YELP data as the source domain to train models. By freezing the backbone and retraining a linear task-specific classification head, we evaluate the linear readout of each model on a series of out-of-domain target tasks, including IMDB, YELP-2, SST-2, SST-5, MR, Amazon-2 and Amazon-5. Each linear head is trained with 1000 labeled data from the target task. As shown in the table below, on the all target tasks, InfoR-LSF consistently achieves the highest improvement. We conjecture the reason to be our method's ability of extracting more versatile features and thus the learned representation is more likely to cover the useful features in target domains, leading to better out-of-domain generalization. In addition, you can also refer to Section 3.2 of the submitted revised paper.

| Model             | Target Dataset |           |           |           |           |           |           |           |
| ----------------- | :------------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |
|                   |      YELP      |  YELP-2   |   IMDB    |   SST-2   |   SST-5   |    MR     | Amazon-2  | Amazon-5  |
| BERT              |     65.81      |   94.95   |   88.24   |   86.54   |   44.88   |   80.70   |   81.59   |   54.53   |
| VIBERT            |     66.00      |   95.87   |   88.05   |   83.90   |   44.75   |   81.20   |   81.81   |   56.05   |
| InfoR-LSF         |   **66.31**    | **95.89** | **88.55** | **88.19** | **46.28** | **82.00** | **83.03** | **57.43** |
| $\mathbf{\Delta}$ |      +0.5      |   +0.94   |   +0.31   |   +1.65   |   +1.4    |   +1.3    |   +1.44   |   +2.9    |

Reviewer comment (Presentation):
An algorithm table would help a lot in method clarity.

Author response:
**Weakness  2**: We provide an algorithm table in Appendix C, which we hope will help you better understand the framework of InfoR-LSF.

Reviewer comment (Experiments):
Why does the feature erasure done in sample space, and not in feature space?

Author response:
**Question 2**: 
Yes, in our work, feature erasing is done at the level of input features, instead of the level of representation features. 

If we try to do feature erasing in feature space, one straightforward way is to mask some mainline features. However, this operation do not necessarily relieve supplemental features out, but instead possibly do learn duplicate mainline features that are masked. 

Another way is to minimize the mutual information between the mainline features and the supplemental features, as questioned by the first reviewer. We copy the answer below:

 We do not directly minimize the mutual information between $\mathbf{z}_M$ and $\mathbf{z}_S$ for two reason. On the one hand, our motive is to relieve the supplemental features $\mathbf{z}_S$ (which are possibly redundant relevant features) from the suppression of mainline features $\mathbf{z}_M$. $\mathbf{z}_S$ and $\mathbf{z}_M$ may be mutually redundant on the training set, that is, they can be different features with high mutual information between each other. Minimizing the mutual information between $\mathbf{z}_M$ and $\mathbf{z}_S$ will also suppress the supplemental features $\mathbf{z}_S$ and cannot achieve our goal. On the other hand, in our framework,  both  $I(\mathbf{z}_M, \mathbf{y})$  and $I(\mathbf{z}_S, \mathbf{y})$  should be maximized, and this conflicts with minimizing the $I(\mathbf{z}_M, \mathbf{z}_S)$. While $I(\mathbf{z}_S;\mathbf{x}|\mathbf{x}')$ represents the information $\mathbf{z}_S$ contains which is unique to $\mathbf{x}$ and is not predictable by observing $\mathbf{x}'$, the goal is to let $\mathbf{z}_S$ learn features different from $\mathbf{z}_M$ .

Reviewer comment (Experiments):
The three-stage supervised learning framework has several components. An ablation study detailing the contribution of each component to the final performance would have given insights into the necessity and utility of each part.

Author response:
We update the original ablation experiment and conduct more specific ablation experiments on the hyperparameters $\alpha$ and $\beta$. Please refer to Section 3.5 of the submitted revised paper for experimental results and conclusions.

Reviewer comment (Reproducibility):
Apart from the sensitivity analysis of coefficient α, the paper does not deeply dive into how sensitive the model is to other hyperparameters, which can be crucial for replication and understanding model robustness.

Author response:
We update the analysis experiments and add analysis on masking ratio, another important hyperparameter of our method. For specific experiments, please refer to Section 3.4 of the submitted revised paper.

Reviewer comment (Experiments):
Why were CIFAR10, IMDB, and YELP selected as the benchmark datasets? Are there plans to test the method on more diverse or domain-specific datasets?

Author response:
We choose CIFAR10 because it was commonly used for image classification. We have also made experiment on CIFAR100, and the results are put in Appendix A.3 of the submitted revised paper. As to text classification, we choose IMDB and YELP, just because their texts are relatively long and likely to contain redundant relevant features.
In addition, we have applied our method on two regression datasets, please refer to answer of Weakness 1 for more details.

Reviewer comment (Experiments):
How generalizable is the InfoR-LSF framework? Can it be easily adapted to other tasks or domains outside of image and text classification?

Author response:
The principle of information retention is general, and the framework for learning supplemental features is also easy to adapt to other tasks. But some technical particularities need attention, such as the saliency erasing mechanisms. 
Actually, we have adapted InfoR-LSF to regression tasks as in the answer of Weakness 1.

Reviewer comment (Experiments):
Given that adversarial training methods like FGSM were considered as baselines, did the authors consider evaluating the adversarial robustness of models trained with InfoR-LSF?

Author response:
The reason of using FGSM as baseline is that FGSM also erases the learned features from the original examples and thus generates adversarial examples, which is somewhat similar to our approach. 
The difference is that FGSM uses the adversarial examples as data augmentation which require the augmented examples be of the same labels as the original examples; but our approach uses the modified example to exert the constraint of conditional mutual information $I(\mathbf{z}_S;\mathbf{x}|\mathbf{x}')$ as in Equation (4), which does not suffer from the label-consistency limitation of data augmentation.
Currently, we have not made any attempts to check the adversarial robustness of InfoR-LSF, but we think it could be a future direction.

Reviewer comment (Presentation):
The presentation can be greatly improved. For instance, out-of-domain scenarios are mentioned in the abstract, but they are not explored in later experiments. The two examples in the introduction are unnecessary; they might even hinder comprehension, as some context is missing (as in example 1.1), and the usage could be restricted by the examples provided.

Author response:
Thank you for your suggestion. We have checked the writing and moved the two examples from Section 1 to the Appendix D in the submitted revised version. In addition, we have provided out-of-domain experiments in the revised version. Implementation and results are as follow:

We believe that the learned representations of InfoR-LSF have the ability to generalize outside the domain since InfoR-LSF is wedded to learn more versatile relevant features. To verify this hypothesis, we choose the sentiment classification task, and use the full-size YELP data as the source domain to train models. By freezing the backbone and retraining a linear task-specific classification head, we evaluate the linear readout of each model on a series of out-of-domain target tasks, including IMDB, YELP-2, SST-2, SST-5, MR, Amazon-2 and Amazon-5. Each linear head is trained with 1000 labeled data from the target task. As shown in the table below, on the all target tasks, InfoR-LSF consistently achieves the highest improvement. We conjecture the reason to be our method's ability of extracting more versatile features and thus the learned representation is more likely to cover the useful features in target domains, leading to better out-of-domain generalization. In addition, you can also refer to Section 3.2 of the submitted revised paper.

| Model             | Target Dataset |           |           |           |           |           |           |           |
| ----------------- | :------------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |
|                   |      YELP      |  YELP-2   |   IMDB    |   SST-2   |   SST-5   |    MR     | Amazon-2  | Amazon-5  |
| BERT              |     65.81      |   94.95   |   88.24   |   86.54   |   44.88   |   80.70   |   81.59   |   54.53   |
| VIBERT            |     66.00      |   95.87   |   88.05   |   83.90   |   44.75   |   81.20   |   81.81   |   56.05   |
| InfoR-LSF         |   **66.31**    | **95.89** | **88.55** | **88.19** | **46.28** | **82.00** | **83.03** | **57.43** |
| $\mathbf{\Delta}$ |      +0.5      |   +0.94   |   +0.31   |   +1.65   |   +1.4    |   +1.3    |   +1.44   |   +2.9    |

Reviewer comment (Evaluation):
Section 3.3 shows the proposed algorithm extracts more information than other algorithms. It is also necessary to show that the extracted information is actually relevant information.

Author response:
We have tested the accuracy of the classification head attached to $\mathbf{z}_S$, and it is even slightly higher than the head of $\mathbf{z}_M$, which manifests the learned supplemental features are relevant to the target label. PS. The cross entropy loss can be thought of as a lower bound of mutual information.

Reviewer comment (Experiments):
It would be helpful to provide some visualization to show that the second stage does erase the salient features.

Author response:
We provide a visual example in Appendix E of the submitted revised paper. In the visual example, we present the gradient distribution of BERT, $\mathbf{z}_M$ head and $\mathbf{z}_S$ head of InfoR-LSF on a test case of IMDB. The depth of the color represents the magnitude of the gradient and it can be observed that $\mathbf{z}_S$ turly erases salient features of $\mathbf{z}_M$ ("movie a 1 .") and learns new features ("made me sick .") which are also helpful for classification.

Reviewer comment (Novelty):
Some context in information theory is missing. A section of related work is needed.

Author response:
We have added related work in the submitted revised paper.

Reviewer comment (Theory):
How is Equation (5) and (6) exactly derived?

Author response:
Equation (5) is the loss function of the variational information bottleneck. Please refer to the paper "Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy (2017) Deep variational information bottleneck" for derivation. The exact derivation of Equation (6) is provided in Appendix B of the submitted revised paper.

Reviewer comment (Novelty):
The paper is unqualified to be a top conference paper in several aspects, including novelty, contribution, and writing quality.

Author response:
We would like to summarize the contribution and novelty here:
1) At the principle level, we propose Information Retention, to use as much relevant information of the input as possible in making prediction.

2) At the technical level, we designed a three-stage framework which is able to extract diverse features by relieving supplemental features from the suppression of mainline features.

3) At the experimental level, we conducted a variety of experiments to verify the effectiveness of the proposed InfoR-LSF method. Its learned representations have exhibited good in-domain performance (especially in the resource-limit scenarios) and good out-of-domain generalization ability.

Reviewer comment (Experiments):
The experiment design is outdated as of 2023.

Author response:
We have added a number of experiments in the submitted revised paper. It includes:
1) Experiments to validate the out-of-domain generalization of the learned representation
2) Experiments on two regression tasks (one for text regression, and the other for tabular regression)
3) Experimental results to report the computational costs.
4) Sensitivity analysis of the main hyperparameters
5) Ablation analysis to indicate the effectiveness of proposed components.

Reviewer comment (Novelty):
The paper lacks a comprehensive literature review.

Author response:
We have added related work to the revised paper.

Reviewer comment (Novelty):
What contributions can the research in this paper make to the current mainstream deep learning models, such as GPT and Diffusion?

Author response:
We think our approach could be applied if there are supervised signals or labels used. We also believe it has wide scope of applicability. In building large language models, the pretext tasks are usually self-supervised and it is possible to get our approach involved in. However, due to  resource limitation, we do not have a chance or plan to do such work. But we plan to study how to adapt our approach to self-supervised contrastive representation learning on images.

Reviewer comment (Presentation):
The method can be better described with an overall workflow or architecture.

Author response:
In order to better understand the workflow of our method, we provide an algorithm table in Appendix C and a variable relationship diagram in Appendix B in the submitted revised paper. We hope this can help you clearly understand our method.

Reviewer comment (Experiments):
Although I believe that the proposed approach can benefit out-of-domain generalization, it would have been better if some preliminary experiments had been conducted.

Author response:
We believe that the learned representations of InfoR-LSF have the ability to generalize outside the domain since InfoR-LSF is wedded to learn more versatile relevant features. To verify this hypothesis, we choose the sentiment classification task, and use the full-size YELP data as the source domain to train models. By freezing the backbone and retraining a linear task-specific classification head, we evaluate the linear readout of each model on a series of out-of-domain target tasks, including IMDB, YELP-2, SST-2, SST-5, MR, Amazon-2 and Amazon-5. Each linear head is trained with 1000 labeled data from the target task. As shown in the table below, on the all target tasks, InfoR-LSF consistently achieves the highest improvement. We conjecture the reason to be our method's ability of extracting more versatile features and thus the learned representation is more likely to cover the useful features in target domains, leading to better out-of-domain generalization. In addition, you can also refer to Section 3.2 of the submitted revised paper.

| Model             | Target Dataset |           |           |           |           |           |           |           |
| ----------------- | :------------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |
|                   |      YELP      |  YELP-2   |   IMDB    |   SST-2   |   SST-5   |    MR     | Amazon-2  | Amazon-5  |
| BERT              |     65.81      |   94.95   |   88.24   |   86.54   |   44.88   |   80.70   |   81.59   |   54.53   |
| VIBERT            |     66.00      |   95.87   |   88.05   |   83.90   |   44.75   |   81.20   |   81.81   |   56.05   |
| InfoR-LSF         |   **66.31**    | **95.89** | **88.55** | **88.19** | **46.28** | **82.00** | **83.03** | **57.43** |
| $\mathbf{\Delta}$ |      +0.5      |   +0.94   |   +0.31   |   +1.65   |   +1.4    |   +1.3    |   +1.44   |   +2.9    |

Reviewer comment (Theory):
To my understanding, the supplemental features are learned by minimizing the conditional mutual information \( I(\mathbf{x}, \mathbf{z}_S|\mathbf{x}’) \), is it right? Why not just minimizing the mutual information between \( \mathbf{z}_S \) and \( \mathbf{z}_M \)?

Author response:
This is a good question. We do not directly minimize the mutual information between $\mathbf{z}_M$ and $\mathbf{z}_S$ for two reason. On the one hand, our motive is to relieve the supplemental features $\mathbf{z}_S$ (which are possibly redundant relevant features) from the suppression of mainline features $\mathbf{z}_M$. $\mathbf{z}_S$ and $\mathbf{z}_M$ may be mutually redundant on the training set, that is, they can be different features with high mutual information between each other. Minimizing the mutual information between $\mathbf{z}_M$ and $\mathbf{z}_S$ will also suppress the supplemental features $\mathbf{z}_S$ and cannot achieve our goal. On the other hand, in our framework,  both  $I(\mathbf{z}_M, \mathbf{y})$  and $I(\mathbf{z}_S, \mathbf{y})$  should be maximized, and this conflicts with minimizing the $I(\mathbf{z}_M, \mathbf{z}_S)$. While $I(\mathbf{z}_S;\mathbf{x}|\mathbf{x}')$ represents the information $\mathbf{z}_S$ contains which is unique to $\mathbf{x}$ and is not predictable by observing $\mathbf{x}'$, the goal is to let $\mathbf{z}_S$ learn features different from $\mathbf{z}_M$ .