Paper: Cross-modality debiasing: using language to mitigate sub-population shifts in imaging (ICLR-2024)

Reviewer comment (Evaluation):
A concern regarding the applicability of this method is that the experiments show that this approach requires significant prompt engineering. But the assumption in this paper seems to be that there are no labels available (e.g., no male/female labels). That means that it wouldn't be possible to compare the performance of prompts like it was done, e.g., in table 1.

Author response:
We agree with the reviewer that  there are challenges in selecting appropriate text prompts when dealing with language (text) related deep models,  and varying text prompts have a significant influence on the final performance, and this work does need a domain explicit validation dataset to guarantee reasonable worst-case performance, as mentioned in Section 3.2 and Section 5 (Experiment) and employed by related work in this line of research. (JTT and $\chi^{2}$-DRO generally use this validation dataset for another purpose, detailed in ‘stable worst-case accuracy across training epochs’ paragraph of section 5.1).
However, our solution enables fine-tuning an extra adapter (in the last layers) with language guidance to mitigate subpopulations in the label space, which follows the trend of the broader and broader application scenarios of the (large) multi-modal models. We believe this type of condition closely satisfies real-world conditions instead of assuming it is a specific condition.

Reviewer comment (Reproducibility):
It is unclear how one would select a debiasing prompt when there is no ground truth labels. Relatedly, it is unclear whether multiple debiasing prompts can be used or how a practitioner would know if they have enough data for the method. These practical questions seem more important than questions about how L-DRO interacts with other methods, but the paper appears to leave them for future work.

Author response:
We agree with the reviewer that  there are challenges in selecting appropriate text prompts when dealing with language (text) related deep models,  and varying text prompts have a significant influence on the final performance, and this work does need a domain explicit validation dataset to guarantee reasonable worst-case performance, as mentioned in Section 3.2 and Section 5 (Experiment) and employed by related work in this line of research. (JTT and $\chi^{2}$-DRO generally use this validation dataset for another purpose, detailed in ‘stable worst-case accuracy across training epochs’ paragraph of section 5.1).
However, our solution enables fine-tuning an extra adapter (in the last layers) with language guidance to mitigate subpopulations in the label space, which follows the trend of the broader and broader application scenarios of the (large) multi-modal models. We believe this type of condition closely satisfies real-world conditions instead of assuming it is a specific condition.

Reviewer comment (Evaluation):
Did the authors explore why their model fails on ViT-L/14? This seems like useful information for practitioners. For example, can they only expect this model to work on smaller models?

Author response:
Please refer to Appendix C, we have explored the failure mode on ViT-L/14. The root cause is the inconsistency of the effectiveness of text prompts over various models. Employing a prompt that is different from the default one achieves a more reasonable performance, it shows that the proposed method outperforms other baselines, as shown in Table 10.

Reviewer comment (Evaluation):
My understanding is that the results in table 6 should be comparable to the 3rd and 7th rows in table 2. There zero-shot learning gets 70.6% worst-case performance. It seems that $\chi^2$-DRO is the only baseline that gets higher than this (but with huge variance). It seems odd that all the DRO methods do worse than not doing anything at all.

Author response:
Table 6 empirically verifies the potential of L-DRO to help stabilize the existing methods since they are orthogonal, and it demonstrates that the proposed method embedded with other baselines generally improves the performance with CelebA but worsens it with the Waterbirds dataset. And, it is not surprising that some baseline methods actually worsen the performance compared with zero-shot performance such as ERM and JTT. Actually, it is well-established that inappropriate fine-tunings damage the distributional robustness of pre-trained models [1,2,3],  which is also mentioned in the introduction. 

It should be noted that parameter-efficient tuning, such as tuning the final layers only, demonstrates popularity under certain large model conditions. The existing work of DRO such as JTT typically tunes the whole model.

Reviewer comment (Evaluation):
The results in table 3 seem to suggest that if the dataset isn't large enough, the proposed method actually harms the results. It would be useful if the authors could (1) provide insight into why this is the case, and (2) provide guidance on how a practitioner can know if they have enough data for the method to be applicable.

Author response:
It is true that training with fewer data points or data that are not enough to represent the whole distribution manifest performance decreasing. When data is not enough to represent the whole distribution, then the updating direction is hard to find the right one or even in the opposite direction.
Table 3 shows the proposed method is data efficient and is possibly due to its parameter-efficient structure.

Reviewer comment (Experiments):
I think the consistency loss might be a bit contradictory to the debiasing loss since the consistency loss encourages the adapter's output to be similar to original image features while the debiasing loss wants the adapter's output to be different from original image features. How do the authors think of / solve the problem? Is there any ablation on the coefficient of the consistency loss?

Author response:
In our loss design, $\eta$ is expected to balance the two items, consistency loss and similarity loss. And we reported the performance under CelebA dataset and network structure CLIP (ViT-B/32) with varying $\eta$ here to show that the proposed method is not sensitive to the hyper-parameter.

η = 0.1, Avg. Acc. = 0.845±0.006, W.C. Acc. = 0.782±0.016.

η = 0.2 (default), Avg. Acc. = 0.836±0.003, W.C. Acc. = 0.792±0.013.

η = 0.3, Avg. Acc. = 0.845±0.003, W.C. Acc. = 0.771±0.028.

η = 0.4, Avg. Acc. = 0.844±0.004, W.C. Acc. = 0.777±0.020.

η = 0.6, Avg. Acc. = 0.844±0.008, W.C. Acc. = 0.763±0.035.

η = 0.8, Avg. Acc. = 0.842±0.006, W.C. Acc. = 0.771±0.037.

η = 1.0, Avg. Acc. = 0.842±0.007, W.C. Acc. = 0.764±0.040.

Reviewer comment (Evaluation):
It's interesting that L-DRO can also improve average loss in Tab 1. Can authors further explain on this phenomenon?

Author response:
We appreciate your careful observation of Table 1 that the improvements in worst-case accuracy seem to slightly improve the average accuracy under varying text prompts with RN50. 
We believe it may imply that our method improves the performance of the worst-case group meanwhile it will least damage the the performance of other groups in such conditions. So that the overall performance (average accuracy) gets improved.
We conducted the following simple experiments. Under CelebA, CLIP (RN50), and text prompts "a picture of [] people", we report the average accuracy, worst-case accuracy, and additional accuracy for each subgroup over ten seeds.

Zero-shot - acc_avg= 0.837, acc_worst = 0.757, acc_each_group = [0.833, 0.757, 0.808, 0.907],

L-DRO - acc_avg = 0.859, acc_worst = 0.789, acc_each_group = [0.789, 0.814, 0.808, 0.908].

The above results support our claims.

Reviewer comment (Writing):
Shouldn't the `y_p` in `{y_p, y_n} := {blond, not blond}` in the second paragraph of Sec4 be `y_b` to be coherent with the notation in the next paragraph?

Author response:
We really appreciate your careful reading and thank you for pointing out the typo (y_p); it has been corrected (See the uploaded revised PDF). Your attention to detail is invaluable.

Reviewer comment (Theory):
While the method is motivated by the general notion that aligned representations from multimodal models can share robustness, the algorithm is restrictive — It primarily facilitates the use of text prompts to learn the debiased representation adapter for improved classification. Extending the algorithm to incorporate other combinations of modalities may be challenging, particularly for modalities where designing debiasing prompts (e.g., in vision) might be difficult.

Author response:
We admit the challenges in designing debiasing guidance from other modalities instead of language modality. And, our solution enables fine-tuning an extra adapter in the last layers with language guidance to mitigate subpopulations in the label space, it follows the trend of the broader and broader application scenarios of the (large) multi-modal models, relying on the guidance from natural language in learning (such as in-context learning), and parameter-efficient fine-tuning regime, which we believe closely matches real-world conditions.

Reviewer comment (Experiments):
Can L-DRO be adapted for combinations of modalities other than vision and language, or does the methodology intrinsically require assistance from the language modality?

Author response:
In current work, there are no such attempts to adapt L-DRO for modalities other than vision and language, however, we believe the principle developed in this work can be easily adapted to other types of data modalities.

Reviewer comment (Evaluation):
Table 4 offers interesting insights. In the rows where the source and target are misaligned, the average accuracy is comparable to that of a zero-shot scenario, however, there is a significant divergence in worst-case accuracy. Does this suggest other subgroups are either adversely affected or disproportionately benefited (so that the average scores can remain similar)? Does this imply that the algorithm still works with other influential attributes even if they are not directly related to the target domain?

Author response:
For Table 4, there is no clear pattern to describe divergences in worst-case accuracy with unaligned semantic relations currently. We do have similar observations that subgroups are (minorly) adversely affected, it serves as fundamentals of combining multiple debiasing sources, specifically, the last two rows.

Reviewer comment (Theory):
One significant concern revolves around the limitation of entropy maximization. As entropy reaches its maximum on a uniform distribution, the analytic solution of $max \ \ell_{ent}$ can be obtained through $(A_{\theta_A} \odot I(\mathbf{x}))^T (T(\hat{t}_1) - T(\hat{t}_2)) = 0$. This occurs when $(A_{\theta_A} \odot I(\mathbf{x})) $ is projected onto the null-space of $(T(\hat{t}_1) - T(\hat{t}_2))$. However, reducing a single rank is insufficient to eliminate the entire spurious representation. The empirical evidence of this limitation can be observed in Table 5 that penalizing semantically correlated sources even increases worst-case accuracy in zero-shot classification.

Author response:
We appreciate the insights regarding our method. Projecting all the debiased feature representation onto the null-space of $[T(\hat{t_{1}}), T(\hat{t_{2}})]^{T}$ is not the only goal since our solution also encourages the similarity of feature representations before and after the adapter with a consistency loss.
Without the consistency loss, we believe all the feature representations may collapse to, e.g., one fixed direction that is perpendicular with $T(\hat{t_{1}})$ and $T(\hat{t_{2}})$.
However, with our consistency loss and  $\eta$ (scalar to balance the above two terms), our results empirically negate the collapse where the average accuracy regarding the task basically does not change compared with zero-shot learning.

Reviewer comment (Experiments):
Baseline implementation — The worst-group accuracy of JTT reported in [1] stands at 86.7% and 81.1% on Waterbirds and CelebA, significantly higher than the results presented in this paper.

Author response:
Apologies for any confusion. A quick answer to the misalignment is due to this work only tunes a simple adapter network instead of full model tuning such as JTT and other baselines in the original papers. We mentioned this part in our objective Equation 4 and highlighted the architecture as I▷A▷T in Table 2. To further clarify, we added additional footnotes in Table 2 - only adapter A is tunable. (See the uploaded revised PDF).
Note that existing works of DRO such as JTT typically tune the whole model while in this work and some large model conditions, parameter-efficient tuning, such as tuning the last two layers only, exhibits popularity.

Reviewer comment (Experiments):
Ablation study — Are there any experimental results for the model trained on the loss defined in Eq.(1)?

Author response:
We want to clarify that ERM (Empirical Risk Minimization) in Table 2 is exactly the result you look for. As expected, ERM generally does not have satisfying performance over sub-population shifts.

Reviewer comment (Experiments):
Table 4 — In some rows, the same source was utilized as the target, resulting in the highest worst-case accuracy. Could you please elaborate on the intention behind this choice and the corresponding performance gain?

Author response:
In Table 4, this work investigates the effect of unaligned debiasing and the combination of multiple debiasing sources. Firstly, the unaligned debiasing does not improve the performance under sub-population shifts. And, the last two rows in Table 4 demonstrate the performance of combining multiple debiasing sources.

Reviewer comment (Writing):
Minor corrections — Eq. and equation are redundantly appeared.

Author response:
Thank you for pointing out the minor issues, and we have  corrected them.