Improved Group Robustness via Classifier Retraining on Independent Splits
Authors: Thien Hang Nguyen
Hongyang Ryan Zhang
Huy Nguyen
Venue: Submitted to ICLR 2023 (2023)
Keywords: spurious correlations
group shifts
overfitting
distributionally robust optimization

Abstract: Deep neural networks learned by minimizing the average risk can achieve strong average performance, but their performance for a subgroup may degrade, if the subgroup is underrepresented in the overall data population. Group distributionally robust optimization (Sagawa et al., 2020a, GDRO) is a standard baseline for learning models with strong worst-group performance. However, GDRO requires group labels for every example during training and can be prone to overfitting, often requiring careful model capacity control via regularization or early stopping. When only a limited amount of group labels is available, Just Train Twice (Liu et al., 2021, JTT) is a popular approach which infers a pseudo-group-label for every unlabeled example. The process of inferring pseudo labels can be highly sensitive during model selection. To alleviate overfitting for GDRO and the pseudo labeling process for JTT, we propose a new method via classifier retraining on independent splits (of the training data). We find that using a novel sample splitting procedure achieves robust worst-group performance in the fine-tuning step. When evaluated on benchmark image and text classification tasks, our approach consistently reduces the requirement of group labels and hyperparameter search during training. Experimental results confirm that our approach performs favorably compared with existing methods (including GDRO and JTT) when either group labels are available during training or are only available during validation.

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IMPROVED GROUP ROBUSTNESS VIA CLASSIFIER RE- TRAINING ON INDEPENDENT SPLITS
Anonymous authors Paper under double-blind review
ABSTRACT
Deep neural networks learned by minimizing the average risk can achieve strong average performance, but their performance for a subgroup may degrade, if the subgroup is underrepresented in the overall data population. Group distribution- ally robust optimization (Sagawa et al., 2020a, GDRO) is a standard baseline for learning models with strong worst-group performance. However, GDRO requires group labels for every example during training and can be prone to overfitting, often requiring careful model capacity control via regularization or early stopping. When only a limited amount of group labels is available, Just. Train Twice (Liu et al., 2021, JTT) a popular approach which infers a pseudo-group-late unlabeled example. The process of inferring pseudo labels can be highly sensitive during model selection. To alleviate overfitting for GDRO and the pseudo labeling process for JTT, propose splits (of the training data). We find that using a novel sample splitting procedure achieves robust worst-group performance in the fine-tuning step. When evaluated on benchmark image and text classification tasks, our approach consistently re- duces the requirement of group labels and hyperparameter search during training. Experimental results confirm that our approach performs favorably compared with existing methods (including GDRO and JTT) when either group labels are available during training or are only available during validation.
for every
independent method via classifier retraining
INTRODUCTION
For many tasks, deep neural networks (DNNs) are often developed where the test distribution is identical to and independent of the train distribution, which can be referred to IID generalization. The performance of a DNN also known to worsen when the testing distribution differs from the training distribution. This problem often referred to dutto--distribution (OOD) generalization. OOD generalization is crucial in safety-critical applications such as self-driving cars (Filos et al., 2020) medical imaging (Oakden-Rayner al., 2020). Hence, addressing the problem of OOD generalization is foundational for real-world deployment of deep learning models.
notable setting where OOD generalization problems appear is the group-shifi setting, where different groups of the data may have distribution shift. In this setting, there are predefined attributes that divide the input space into different groups of interest. Here, the goal performs well across several predefined groups (Sagawa et al., 2020a). Similar to other problems in OOD generalization, DNNs learned by empirical risk minimization (ERM) are observed to suffer from poor worst-group performance despite good average- group performance.
find model that
The difficulty with learning group robust DNNs can be attributed to the phenomenon of shortcut learning (Geirhos et al., 2020) or spurious correlation (Sagawa et al., 2020a; Arjovsky et al., 2019). Shortcut learning poses that ERM favors those models that discriminate based on simpler and/or spurious features of the data. However, one wishes for the learning algorithm to produce model that uses features (i.c. correlations) that performs well not only on the train distribution, but also on all potential distributions that task may generate, like that of worst-group distribution.
In recent years, the group-shift setting has received considerable attention, where Sagawa et al. (2020a) first investigates distributional robust optimization (DRO) (Duchi et al., 2021; Ben- Tal et al., 2013) in this setting and introduces Group DRO (GDRO) that attempts to directly optimize for the worst-group error. Since then, GDRO has been the standard method for producing group-robust

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GDRO (vanilia) using all labels with &y tuning
CROIS using 30% labels with defoult tuning (42 10
Validaion Wozz Acorscy
Validaionn Worss group Acouracy
0.00
0.00
0.60
0.60
0.40
0.40
0.20
0.20
Train
Train
Validation
0.00
Validacion
D0
100 1.0
2010 250
100
150
200 250
Epoch
Worst-group learning curve on the Waterbird dataset between GDRO (left) and CROIS (right) from the setting in Section 4.2. In the left figure, the validation accuracy of GDRO becomes unstable as the number of epochs increase beyond 100 while the training accuracy remains close to 100%. Our approach CROIS instead uses 30% of the training data with group labels for fine-tuning the classification layer of a DNN that is obtained via ERM using the rest of the training data without group labels (see also Algorithm 1). This allows CROIS to reuse ERM features while improving generalization compared with GDRO.
Figure 1:
models. However, GDRO can sensitive towards model capacity (Sagawa et al., 2020a) and require group labels for all examples at training time.
As group annotations can be expensive to obtain, many works consider ways to reduce the amount of groups labels needed (Liu et al., 2021: Creager et al., 2021; Nam et al., 2022). These methods usually follow the framework of first inferring sseudo-group-labee (pseudo-labeling) and then applying group-robust algorithm like GDRO the pseudo-labelled data. While results for these methods have been promising, they usually introduce several more sensitive hyperparameters that be expensive tune. Our work aims to obtain group robust models using as few group labels as possible while alleviating the need to carefully control model capacity.
using a certain referenced model
Our contribution. In this work, propose simple approach, called CROIS. By foregoing the error-prone and costly pscudo-labeling phase to instead concentrate on efficiently utilizing group labels by applying them only to the final classifier layer, CROIS achieves good robust performance without relying on multitude of hyperparameters and large scale tuning, which has been growing concern in the community (Gulrajani and Lopez-Paz, 2021).
In short, CROIS takes advantage of the features learned by ERM (Kang et al., 2019; Menon et al., 2021) while overcoming the deficiency of its memorization behavior (Sagawa al., 2020b) by utilizing the training data as two independent splits: one unnubeedl split to train the feature extractor and one group-labeled split to retrain only the classifier with a robust algorithm like GDRO. We demonstrate through ablation studies that the of independent splits is crucial for robust classifier retraining. Furthermore, CROIS's restriction of GDRO to only a low-capacity linear layer reduces GDRO's 's sensitivity towards model capacity well the amount of data needed for GDRO to generalize well (e.g. Figure I and Figure 3).
For various settings where group labels only partially available during training (Section 4.1), experimental results on standard datasets including Waterbird, CelebA, MultiNL. and CivilComments show improved performance over existing methods including JTT (Liu et al., 2021) and SSA (Nam et al., 2022), despite minimal parameter tuning and no reliance on pseudo labeling. In another setting where more group labels are available (Section 4.2), even when using only a fraction of the available group labels, our competitive robust performance against GDRO demonstrates our method's ''s label efficiency. Finally, our results provide further evidences of ERM trained DNNs containing good features on both image classification (Menon et al., 2021) and natural language classification tasks.
1.1 RELATED WORKS
There are three main settings for the group-shif problem: (1) full availability of group labels, (2) limited availability group labels, and (3) no availability of group label. Other related areas include domain generalization and long-tailed classification.
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Full training group labels. Most methods here revolve around up- up-weighing minority groups, subsampling minority groups (Sagawa et al., 2020b), or performing GDRO (Sagawa et al., 2020a). Follow-up works include integrating data augmentation via generative model (Goel et al., 2020) or selective augmentation (Yao et al., 2022) to a robust training pipeline.
Limited access to group labels. In this setting, the approach of inferring more group labels for the group-unlabeli data remains the most popular. These pseudo- group-labels are usually generated by training a referenced model that performs the labeling. For example, (Liu et al., 2021) utilizes low-capacity model that create groups by labeling whether an example is correctly classified by the referenced model or not. Similarly, works like (Creager al., 2021: Dagaev al., 2021; Krueger et al., 2021; Nam et al., 2022) and (Nam et al., 2020) are variants of this approach of inferring pseudo- saauudo-roup-lbbell. These methods then proceed to use group-robust algorithm like GDRO (Sagawa et al., 2020a) or Invariant Risk Minimization (IRM) (Arjovsky et al., 2019) to perform robust training on a new network with the newly generated pseudo-group -raoo---abuss.......
No group labels. This setting removes the ability to validate as well as knowledge of potential groups. This makes the problem more difficult as is unclear which correlation to look for during training. Some theoretical works in this space include Hashimoto et al. (2018); Lahoti et al. (2020). Sohoni et al. (2020) proposes a popular empirical approach in this setting and has popularized the pseudo-labeling and retraining approach. This setting is related to domain generalization. Gulrajani and Lopez-Paz (2021) shows through mass-scale experiments that most OOD generalization methods do not improve over ERM given the same amount of tuning and model selection criterion.
long-tailed classification. The long-tailed problem concerns with certain classes having significantly fewer training examples than others (see for example Haixiang al. (2017) or Zhang et al. (2021) for a survey). Some techniques from the long-tail literature, like Cao et al. (2019), has been applied to the group-shift setting to account for the groups imbalances, as in Sagawa et al. (2020a). Insights from applications of representation learning in the long-tail problem (Kang et al.. 2019) gives valuable evidences for ERM trained DNNs containing good features which is central for our method.
2 PRELIMINARIES
For classification task T of predicting labels in y from inputs in X, we are given training examples {(2,, y4) }i) that are drawn IID from some train distribution D Puain- In the domain generalization setting, we want good performance to Diain through the task T. More explicitly, we wish to find a classifier f from some hypothesis space F using Dirain such that the classification error L(f) = Ee.g A y) of f w.r.t. Dieu is low. This framework encapsulates many problems like adversarial robustness, domain adaptation, long-tail learning, few-shot learning, and the problem considered here: group-shift.
some unknown test distribution Deest that is different but related
In the group-shift setting (Sagawa et al., 2020a), we further assume that associated with each data point x an attribule a(x) (some sub-property or statisties of x)) from set of possible attributes A. These attributes along with the labels form the set of possible groups G = A x y that each example can take. We denote an input x's group label as g(z) E G. We then define the classification enror of a predictor o (w.r.t, fixed implicit distribution) restricted to group E g to be (f) == [e # v). The notion of worst-groug error upper bounds the error of w.r.t. any group Lug(() == maxgeg Lg( Using this notation, the group-shift problem aims to discover classifier in arg min L (f)} = arg min [maxgeg [.(.). } We observe that the group-shift problem is just special case of the domain generalization problem when Dusst is the distribution consisting of only the points (x,,) with g(x) being restricted to the worst-group of ʃ in G. Here, GDRO solves this objective by performing minimax optimization procedure that alternates between the model's weight and relaxed weights on the groups
Spurious Correlations and Memorization. As an example, consider the Waterbird dataset (Sagawa et al., 2020a), where it has been constructed by combining images of water/land birds from the CUB dataset ( (Welinder et al., 2010) with water/land backgrounds from the PLACE dataset (Zhou et al., 2017). The task is to distinguish whether image of a bird is waterbird or landbird. In terms of our problem, the type of bird forms the labels V, and the backgrounds are set to be the attribute AA for each type of bird. Altogether, these form four groups: g = x A (waterbird, landbird) (water, land).
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Algorithm 1 Classifier Retraining on Independent Splits (CROIS)
1: Input: Training data D,. with group labels and training data without group labels Dr. Classifier retraining algorithm R (default to GDRO), Optional splitting parameter p (default to 1).
2: Obtain validation sets by partitioning Dr into D', and DL_ma) and Du into Dir and D ((rat)
3: (Optional) Add more unlabeled data via split proportion p: Partition D'2 into two parts, D1 and
D2 such that Dil = (1 - p) |D']l and |D2] p [P'). Set D/2 + D2 and D' f D' U D
4: Obtain the initial model [ by running ERM on D'y and selecting the best model in terms of average accuracy D) U D(val)
5: Perform classifier retraining R with feature extractor f on D', and then select the best model in
terms of worst-group accuracy on D(rat) the final output.
This dataset is constructed so that the proportion of birds on matching backgrounds is significantly more than those of the mismatched backgrounds. This is so that the backgrounds could be spuriously correlated with the labels, as predicting the background alone would achieve a high average accuracy w.n.t the train distribution already. As expected, for ERM trained models, the groups with the highest error are the minority groups where the background mismatches the type of the bird, suggesting that the model is actually predicting using the background instead of the bird. Furthermore, the fact that these high-capacity models achieve zero training error leads to the conclusion that these models not only utilize spurious features like the background to make its predictions, but also must have memorized the minority groups during its training process (Sagawa et al., 2020b). These problems are common when there is data imbalance (Feldman and Zhang, 2020). In the next section, we propose a method that attempts circumvent these issues.
3 METHOD: CLASSIFIER RETRAINING ON INDEPENDENT SPLITS
Algorithm presents an outline for our main method: Classifier Retraining On Independent Splits or CROIS. Given group-labeled data and group-unlabeled data, CROIS involves several steps: (1) organize the data into one group-labeled split D'2 and one group-unlabeled split D':, (2) obtain an ERM trained feature extractor with the group-unlabeled split D',, and (3) perform robust classifier retraining with the group-labeled split D/, where classifier retraining refers to fine-tuning the final
linear layer of a DNN. In the setting where group labels are limited (as in Section 4.1). [D] is much smaller than |Dyl and we do not need set p < 1. There, primarily concern with partitioning
D2 into D'2 and ((aa)) On the other hand, when training group labels are available (as in Section 4.2) and [Dy] is much smaller than [D,), the optional parameter p in step 3 controls the size of Dig to obtain a feature extractor and the amount of group labels D'2 actually used at train time.
Motivation.
Inspired by previous works that have demonstrated the potential of simple ERM trained DNNs on a variety of OOD tasks (Gulrajani and Lopez-Paz, 2021; Rosenfeld et al., 2021) )) and long-tailed tasks (Kang al., 2019), our method focuses on developing a simple method around ERM trained DNNs. As soon to be discussed, a strength of an ERM trained DNN is that there are convincing evidences that it contains good features. On the other hand, its weaknesses involve memorizing examples and using spurious features. CROIS is designed to alleviate these weaknesses while taking advantage of the good features of an ERM trained DNN.
Models trained by ERM contains good features. While not the first work to notice that ERM trained models contain good features, Kang et al. (2019) demonstrates this hypothesis through extensive experiments on several long-tailed vision datasets to investigate different learning strategies for obtaining a feature extractor as well as ways to fine-tune the classifier layer. There, an ERM trained feature extractor combined with a non-nonnnae non-parametric method of rescaling' the classifier layer achieves (then) state-of-the-aas results on all three datasets. A similar study (Menon et al., 2021) has been done to confirm this hypothesis on vision datasets in the group-shift setting. This suggests that problem (for which the group-shift problem almost always suffers) lies
one key to the in correcting the classifier layer, which forms the first phase of CROIS. It is peculiar that rescaling the classifier works best whereas intuition suggests data-dependent method like classifier retraining would be better. We hypothesize that this is related to the next issue of our discussion.
R Rescaling each row of the linear classifier using the row's norm to some power, See Kang al. (2019).
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Ursen Enamplo
Wanttedd on Waislee Winor
WNootled Land Wioootee
Figure 2: (SNE projection (Van der Maaten and Hinton, 2008) of the features of an ERM trained ResNe150 on seen (left) versus unseen (right) examples from Waterbird." 2 The features of the minority groups (orange and yellow) from the waseen examples appear better separated from the majority groups than that of the seen examples. Using unseen examples plays major role in CROIS's s ability to improve worst-group performance via robust classifier retraining.
Memorization behavior of high capacity DNNs. It has now been well known of high capacity DNNs' ability to memorize training examples (Zhang al., 2017). In the group-shift setting, this behavior has been investigated by Sagawa et al. (2020b), which provides empirical and theoretical justifications for DNNs' memorization behavior of minority groups' training examples. This behavior of memorizing minority examples have also been observed in the broader framework of data imbalances (Feldman and Zhang, 2020). One way circumvent memorization is control the model's capacity by incorporating some combinations of high & regularization, early stopping, and other correctional parameters has been done Sagawa al. (2020a). However, the additional tuning required might be costly. We instead tackle this via independent splits.
Circumventing memorization with independent splits. Examples being memorized must in- evitably impact their ability to be useful in subsequent usage. As memorized examples' (i.c. already correctly classified) loss must be low, their gradients contain little information to of much use. Furthermore, the features of memorized examples might not be representative of their group during test time: Figure presents visualization of the features between seen versus unseen examples. Thus, combining this observation with the evidences for ERM trained DNNs containing good features, CROIS performs robust classifier retraining on unseen examples (D', in Algorithm 1) in hope of learning classifier that utilizes features more representative of examples during test time.
EXPERIMENTS
We conduct experiments in two settings: (1) where group labels are only available from the validation split of the datasets (as in Liu et al. (2021); Nam et al. (2022)); and (2) when fraction of group labels is available from the training split and all group labels are available from the validation split.
similar setup in Liu al. (2021) and Sagawa et al. (2020a). To demonstrate the ease of tuning of CROIS, unless noted otherwise (e.g. Table 2 and parameter p in Table 3), we fix the hyperparameters of both the ERM and the robust classifier retraining phase, reusing standard parameters for ERM (see Appendix A for full hyperparameters and models details). Further results of CROIS with tuned hyperparameters are presented in Section of the Appendix.
Setup. We
Datasets. We experiment on four datasets:
Waterbird (Sagawa et al.. 2020a). Combining the bird images from the CUB dataset (Welinder et al., 2010) with water or land backgrounds from the PLACES dataset (Zhou et al., 2017), the task is classify whether an image contains a landbird or a waterbird without confounding with the background. There are 4795 total training examples, where the minority group (waterbird, land background) has only 56 examples. We report the weighted test average accuracy due to the skewed nature of the val and test sets to be consistent with Sagawa et al. (2020a).
CelebA (Liu et al., 2015) is popular image dataset of celebrity faces. The task is to classify the celebrity in the image is blond or not blond, with male or not male as the confounding attribute. There are 162770 total training examples, and the smallest group (blond, male) has 1387 examples.
Half of the data is used to obtain feature extractor while the other half is used to obtain the features of the examples.
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Table 1: Experimental results for the setting when only group labels from the validation split are used. Results for JTT and SSA are taken from Liu et al. (2021) and Nam et al. (2022), respectively. The numbers in parentheses denote one standard deviation from the mean across 3 random seeds. See Table E in Appendix 17 for comparison with additional baselines.
Waterbird
CelebA
MultiNII
CivilComments
Method
Avg Ace
Wg Acc
Avg Acc
Wg Ace
Avg Acc
Wg Ace
Avg Ace
Wg Acc
JTT
93.9
86.7
88.0
88.1
78.6
72.6
91.1
69.3
SSA
92.2 (0.87)
89.0 (0.55)
92.8 (0.11)
89.8 (1.28)
79.9 (087)
76.6 (0666)
88.2 (1.95)
69.9 (2.02)
CROIS
92.1 (0.29)
90.9 10.12)
91.6 (0.61)
88.5 (0.87)
81.4 (0.06)
77.4 (1211)
90.6 (0.20)
70.3 (0.34)
MultiNLI (Williams et al., 2017) is a natural language inference dataset for determining whether a sentence's 's hypothesis is entailed by, is neutral with, or contradicts its premise. The spurious attribute is the presence of negation words like no, never, or nothing. This task has 6 groups, with 206175 total training and 1521 in the minority group examples (is entailed and contains negation).
CivilComments- WILDS (Borkan et al., 2019; Koh et al., 2021) is natural language dataset where the task is to classify whether a sentence is toxic or non-toxic. There are 8 demographics male, female, white, black, L.GBTQ, Muslim, Christian, and other religion- forming 16 groups that overlap, because a comment can contain multiple demographics. Following Koh et al. (2021), we evaluate on all 16 groups but only use the attribute black along with the label in training. There is a total of 269038 training examples with 1045 minority examples from (other religion, toxic).
4.1 RESULT OF USING GROUP LABELS FROM THE VALIDATION SPLIT
Setup. In this section, we consider the setting where group labels Dr, are available only from the standard validation split, where these group labels can be used for training (Nam et al., 2022) and/or model selection (Liu et al., 2021). Here, the training split is treated as the group-unlabeled set Dy- Most methods in this setting employ some pseudo-labeling approach to generate pseudo-group-labels that are then used to train a new network via a robust algorithm like GDRO. On the other hand, CROIS simply uses half of Dr. for classifier retraining D'2 and the other half for model selection D(unl) and does not rely on pseudo-labeling CROIS also reuses the initial model for the retraining phase, making CROIS closer to that of single phase procedure with additional fine-tume tne-tuning.
Results. In Table 1, we compare CROIS against JTT (Liu et al., 2021) and SSA (Nam et al., 2022), where we report the mean and one standard deviation of the Test Average (Avg Acc) and Worst-Group Accuracy (Wg Acc) across three random seeds. There, CROIS outperforms JTT on all 4 datasets and SSA on datasets using default parameters. Note that, unlike CROIS and SSA, JTT only uses available group labels for model selection. However, JTT requires training large number of models across two phases, which can be expensive. Furthermore, JTT's model selection can be quite sensitive (see Section 5.4 of Liu et al. (2021)). SSA alleviates this problem of JTT by more efficiently utilizing group-labels to infer pseudo-labeling. Finally, our method dispenses altogether with pseudo-labeling while still achieving competitive performance JTT and SSA.
Discussion. We clarify the difference between JTT and CROIS. First, the initial ERM phase of JTT is for inferring gooud--gruu-llaells for the group-unlabeled data. This phase requires careful hyperparameter tuning and capacity control using the group-labeled validation set to accurately produce weeud--roupp-lbeht (as noted in section 5.4 of Liu et al. (2021)). On the other hand, CROIS trains a single ERM model and simply retrains the last layer with any available group labels. Second, JTT's final performance limited by GDRO's performance on the full network, which can be worsen by mislabeled pseudo-labels from the first phase. In contrast, we demonstrate in Section 4.2 that, by limiting GDRO to only the last layer, CROIS is competitive to full GDRO even when using only a fraction group labels and minimal tuning.
Compared with SSA, CROIS does not relying on pseudo labeling. In SSA, there is a phase that performs pseudo labeling and another phase that performs robust training with the inferred group labels. In the first phase. SSA trains a separate network that predicts the group rather than the class. By treating the pseudo-labeling problem as sumi-super learning, SSA's pseudo-labelin capability is shown to improve upon JTT. Our results show that CROIS outperforms SSA on 3 out of 4 datasets while reusing default parameter.

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Table 2: Worst-group test accuracy for partial group-labels from the validation split. Results for SSA and JTT are taken from Table 3 of Nam et al. (2022). The standard deviation is reported based on three independent runs. See Table 18 in Appendix E for comparison with additional baselines.
% of group-lahels from the validation split
CelebA
Waterbird
20%
10%
5%
20%
10%
5%
JTT (Liu et al., 2021) SSA (Nam et al., 2022)
81.1
81.1
82.2
84.0
86.9
76.0
88.9
90.0
86.7
88.9
88.9
87.1
CROIS's Wg Acc
89.6 (0:4)
87.6 (0.6)
87.3 (1.0)
90.4 (1.0)
88.2 (0.9)
87.8 (1.3)
CROIS's Avg Acc
90.8 (0:2)
91.6 (0.3)
87.8 (1:6)
92.4 (0.5)
93.0 (0.7)
88.7 (1.6)
Table 3: Comparison between CROIS and GDRO. NCRT refers to naive classifier retraining i.e. when independent split is not used during the retraining phase. Results marked with are taken from (Sagawa et al., 2020a). "For Waterbird, we omit the result for p = 0.05 due to the small dataset size and not being able to sample any minority-grouy group example for robust retraining.
Waterbird
CelebA
MultiNLI
CivilComments
Method
Avg Ace
Wg Acc
Avg Acc
Wg Ace
Avg Acc
Wg Acc
Avg Acc
Wg Acc
ERM
96.9
69.8
95.6
44.4
82.8
66.0
92.1
63.2
GDRO
93.2
86.0
91.8
88.31
81.4
77.7
89.6 (0.23)
70.5 (2.10)
CROIS' p- group-labeled fraction used for retraining (with
p unlabeled fraction for the ERM phase)
0.05
91.9 (0.50)
88.9 (1.10)
81.8 0.15)
73.8 (1.54)
90.8 (0.40)
63.3 (7.82)
0.10
95.4 (1.10)
83.5 (3:24)
91.3 (026)
90.3 (0.82)
80.8 (0.1))
75.3 (2.06)
89.5 (1.81)
68.7 (1.72)
0.30
90.8 (0.35)
89.6 (1.15)
91.3 (0.44)
90.6 (0.95)
80.0 (031)
77.9 (0.17)
89.7 (0.35)
68.6 (1.53)
0.50
90.4 (.95)
89.5 (0.59)
91.9 (0:35)
88.2 (210)
79.8 (0.26)
74.4 (1.00)
89.5 (0.70)
71.0 (1.50)
NCRT
96.5
75.2
93.9
69.2
82.3
67.9
90.3
67.6
Reducing validation split size. Following the setup in JTT and SSA, we vary the size of the validation split (20%, 10% and 5% of the original) to test whether our results still hold in these settings. We consider both the Waterbird and CelebA datasets. Note that for this setting, CROIS must be additionally funed to account for the increased difficulty of the reduced group-labels quantity. Nevertheless, the smaller examples quantity along with just training the last layer makes the extra tuning not too expensive (details and setup in Section F). We present our results (along with error bars) in Table 2, where we find that CROIS outperforms JTT and SSA on various percentage levels.
4.2 RESULT OF USING PARTIAL TRAINING SPLIT GROUP LABELS
Next, we consider the setting where group labels are available from both the training split and the validation split. In contrast to Section 4.1, the standard validation split is used only for model selection D(vaa) and not for classifier retraining D'2 here. We compare CROIS using some fraction of the training split's group labels against GDRO using all the group labels. Again, we fix parameters of CROIS to standard ERM parameter to demonstrate its mnse-of-tuning (see Appendix A).
Setup. We study CROIS with different amount of training group labels, determined by the parameter p. This means that (1 p) fraction of the training split is used obtain a feature extractor in the first phase D/r (that uses no group label), and the rest p fraction of group labels are used for robust classifier retraining D'. This setup allows examining the trade-off between the quality of the feature extractor versus the amount of data available to perform classifier retraining. Additionally, to demonstrate the importance of retraining with unseen examples, we experiment with robust classifier retraining using the same data from the first phase i.c. without independent splits denoted as NCRT.
The parameter p. In practice, we expect that Dr1 is a lot smaller than |Dul. as in Section 4. There, Table 2 suggests that reasonable robust performance can be achieved with small fraction of group labels. In this setup, however, since the amount of group labels is abundant (Dr = 0 and Dr is large), we treat p tune-able parameter that controls the size of D'y and D',- Furthermore, using p fraction of the available group labels simulates the process of obtaining group labels for a random fraction of the data if one is constraint by budget for obtaining group labels.
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Results. In Table 3, CROIS outperforms GDRO both image datasets and yields competitive performance to GDRO on the two text datasets when using only a fraction of group labels and reusing default hyperparameters. Our result implies that comparable or even better robust performance than GDRO can be obtained by collecting group labels for roughly 30% of the available training data (modulo validation). One exception is in severe group imbalance case in CivilComments (the minority group consists of only 0.4% of the dataset). There, a higher fraction of group-labeled data beneficial to obtain more siinoitt-group examples. Hence, a more efficient sampling method to include more minority examples (e.g. filter by labels first) would be beneficial in practice. Finally, the results for NCRT also show the importance of using independent split for classifier retraining. 3
Trade-off between feature extractor and amount of group-labeled data for robust retraining. From the results across the datasets, allocating more examples towards training the feature extractor (lower p) generally yields higher on-average accuracies. The worst-group error after classifier retraining has a more complex interaction with p. as depends on both the quality of the feature extractor and the amount of group-labeled examples available to perform classifier retraining. While varying the proportion p in our experiments gives a rough estimate of this tradeoff, we hypothesize that the availability of minority group examples is the most important for obtaining a robust classifier. We further support this intuition with an ablation study in Section D.2 where removing non-m non-minority examples has an insignificant impact on the final group-robust performance.
Alleviating GDRO's 's sensitivity towards model capacity. GDRO's requirement for model capacity control via either &g regularization or early stopping is well noted in the literature (Sagawa et al., 2020a). In Table 13, we compare GDRO's s and CROIS' sensitivity towards different &g regularization. While GDRO's s performance on Waterbird is relatively uniform, GDRO is more sensitive different le settings on CelebA. When & for CelebA, we see that GDRO fails altogether (see Figure 3). On the other hand, CROIS achieves consistent performance across different Eg setting. CROIS controls the model capacity by limiting GDRO to only the last layer. This alleviates GDRO's tendency to overfit and simplifies parameter tuning (as in Figure for Waterbird).
GDRD ColebA falls with
CROIS ColobA with
1.00
Train
Validaten Worss group Accuracy
1.00
Volidation
0.80
0.80
0.00
0.00
Validation Worz
0.40
0.40
0.20
0.00
Validucion
0.0
2.5
5.0
7.5
10,0
12.5
15.0
17.5
20.0
0.0
2.5
6.0
7.5
10.0
12.5
15.0
17.5
20,0
Epach
Epach
Figure 3: While GDRO often requires high & regularization to avoid overfitting, setting lz too high might cause instability in GDRO's 's minimax optimization procedure (left).
4.3 ABLATION STUDIES AND DISCUSSIONS
Obtaining a good feature extractor. An ablation study on the effects of different validation accuracies and initial algorithm on the feature extractor's quality (measured by robust performance after classifier retraining) is presented in Section B. Similarly to previous works Kang et al. (2019), we find ERM providing the best features over reweighting or GDRO (of which both require group labels). We find that there's 's positive correlation between validation average accuracy and features' quality. This then serves as proxy for CROIS' model selection criterion the first phase, which significantly simplifies parameter tuning over other two-phase methods in the group-shift setting.
'In Sagawa et al. (2020a), Group Adjustment (GA) (Cao et al., 2019) is observed to improve Waterbird's WG accuracy to 90.5%. We also notice an improvement when GA is incorporated to CROIS and obtain a 90.3% + 0.62 test WG accuracy. We also observe that, similarly to Sagawa et al. (2020a), GA only works for the synthetic dataset Waterhird but not for CelebA nor MuI
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Table 4: Comparison between reweighting, subsampling, and GDRO classifier retraining algorithms on top of the same feature extractor (p = 0.30).
Retraining Method
Waterbird
CelebA
MulIiNLI
CivilComments
Avg Ace
Wg Ace
Avg Ace
Wg Acc
Avg Acc
Wg Acc
Avg Acc
We Acc
Reweighting Subsampling
95.2
87.1
92.1
85.0
78.9
67.0
88.3
56.4
95.8
81.1
91.6
86.1
78.6
64.0
91.8
59.3
GDRO 91.4
90.2
91.6
90.4
80.3
78.0
89.7
69.1
GDRO is better than reweighting and subsampling for classifier retraining. Table 4 contains results on using different classifier retraining methods. We observe that GDRO produces the best group-robust performance (since GDRO is designed for this setting after all). Reweighting and subsampling seem to be effective on the vision datasets, but fail to perform on the NLP datasets.
Table 5: Comparison between retraining with GDRO on the full network (Full) versus last layer (I.L..
GDRO p= 0.30
Waterbird
CelebA
MultiNLI
CivilComments
Avg Acc
Wg Acc
Avg Ace
Wg Acc
Avg Ace
Wg Acc
Avg Acc
Wg Acc
LL (CROIS)
91.4
90.2
91.6
90.4
80.3
78.0
89.7
69.1
Full
90.5
79.8
91.6
78.3
80.8
75.1
90.4
69.1
Classifier retraining outperforms full retraining with GDRO. Classifier retraining plays a central role in method. In Table 5, we compare between fine-tuning with GDRO the full DNN versus just the last layer (L..) for an independent split of p = 0.30 We see that GDRO on the LLI is much better than full GDRO most of the datasets (except CivilComments). However, the main difference is that while LL retraining (i.e. CROIS) requires little additional tuning, we must grid search for different regularization strength for GDRO when applied on the full network. CROIS can be shown to be quite robust to different parameter setting and regularization strength (details in Appendix C.1).
Models learned with ERM contain good features. The positive result for our decoupled training procedure provides another strong evidence for ERM trained models containing good features for the group-shif problem. While this is consistent with findings in the literatures on vision datasets (Kang et al., 2019; Menon et al., 2021), our work further provides some of the first evidences of this hypothesis non-vision tasks, where the same result would not have been possible without independent split, evident in the result for Naive CRT in Table 3.
Simplified model selection. The model selection criterion of picking the best average validation accuracy model simplifies hyperparameter tuning in comparison with other two-phases methods. This decision has been chosen mainly from the ablation experiments in Section B of the Appendix, where we observe that higher average validation accuracy generally suggests better features.
CONCLUSION AND FUTURE WORKS
In this paper, we propose Classifier Retraining on Independent Split (CROIS) as simple method reduce the amount of group annotations needed for improving worst-group performance well as alleviate GDRO's requirement for careful control of model capacity. Our experimental results show the effectiveness of CROIS on four standard datasets across two settings and further provide evidences that ERM trained models contain good features for the group-shift problem.
Future works. The richness of ERM trained DNNs' features can potentially be useful towards solving the seemingly harder group-agnostic setting (where no group label is available) by allowing the practitioner to focus on obtaining a robust classifier given an ERM trained feature extractor, where we have shown that reasonable robustness can be achieved with relatively few group-labels, which makes the problem seem closer in reach. On a broader note, while most works representation learning focus producing good features (either with supervised, unsupervised, or self-supervised approaches), further examinations into different ways to perform classifier retraining in different settings (as in our work) could give fuller picture to the features quality of different methods.

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Reproducibility statement. We include source code in the supplemental material. All imple- mentation details and hyperparameters are detailed in Section of the Appendix.
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Table 6: Hyperparameters used the experiments. The slash indicates the parameters used in the first phase (feature extractor) versus the second phase (classifier retraining).
Waterbird
CelebA
MultiNLI
CivilComments
Learning Rate
10 10-1/10-
10-4/10-4
2 10 x 10-5
10-$/10-5
& Regularization
10 1001/10-
10 /10
0/0
10 2/0
Number Epochs
250/250
20/20
20/20
6/6
A EXPERIMENTAL DETAILS
A.1
INFRASTRUCTURE
We performed our experiment on PCs with one NVIDIA RTX3070 and one NVIDIA RTX3090. Our implementation is built on top of the code base from Liu et al. (2021). Experimental data was collected with the help of Weights and Biases (Biewald, 2020).
A.2 MODELS
We use ResNet50 (He et al., 2016) with ImageNet initialization and natch-normalizatie for CelebA and Waterbird. We use pretrained BERT (Devlin al., 2018) for MultiNI.I and CivilComments. We use the original train-val-test split in all the datasets and report the fest results. Cross-entropy is used as the base loss for all objectives. SGD with momentum 0.9 is used for the vision datasets while the AdamW optimizer with dropout and a fixed linearly-decaying learning rate is used for BERT. We use a batch size of 16 for CivilComments and 32 for the rest the datasets. We do not use any additional data augmentation or learning rate scheduler in our results.
A.3 HYPERPARAMETERS
Table contains the hyperparameters used in our experiments in Sections 4.2 and 4.1. Note that these are the standard parameters for obtaining ERM model for these datasets in previous works (Sagawa et al., 2020a; Liu et al., 2021). The only difference that we train Waterbird and CelebA for slightly shorter epoch due to finding further increase in validation accuracies after those epochs.
In our experiments, unless noted, we do not tune for any other hyperparameters. For the second phase of CivilComments, do not the default regularization but opt for 0 since the linear layer already has low capacity. However, adding further regularization does not seem to have much of an effect as in section C.I.
B ABLATION STUDIES: OBTAINING A GOOD FEATURE EXTRACTOR
B. IMPACT OF THE FEATURE EXTRACTOR'S ALGORITIMMS
Here, we provide evidences that ERM trained models produce the best features for worst-group robustness. We conduct an experiment on Waterbird, where instead of using ERM obtain a feature extractor, we perform GDRO and Reweighing instead on the first phase. The results are in table 7. While using reweighing or GDRO for the first phase defeats the purpose of reducing the amount of group-labels needed (whereas ERM doesn't 'ttt need any), it is informative examine the features alone.
Table Effects of different methods for obtaining feature extractor on test average accuracy and test worst-group accuracy (with ResNet50 on Waterbird).
Feature extractor via
Test Avg Acc
Test Wg Acc
Reweighing
90.1
88.8
GDRO
88.6 90.8
ERM
90.5
90.2
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Here, we that even though ERM does not group-labels, it provides the best features for robust classifier retraining on independent split.
B.2 IMPACT OF EARLY STOPPING AND VALIDATION ACCURACIES ON THE FEATURE EXTRACTOR
In this section, we present an ablation study of how different early stopping epoch (figure 4), average validation accuracy (figure left), and worst-group accuracy (figure 5 right) of the initial ERM trained model (feature extractor) affect the GDRO retraining phase of CROIS. The results here are from performing CROIS with GDRO and p = 0.30 on Waterbird across a wide variety of epochs. Table presents the full data generated for this section.
Taxt Asc PPice
94
Figure 4: The effect of using different epochs for the feature extractor (phase 1) on classifier retraining's 's (phase test accuracies.
(Paaee
(Pewe
Pog Shrne
Via 9SS (Phine
Figure 5: The effect of different validation average accuracies (left) and validation worst-group accuracies (right) from the feature extractor (phase 1) on classifier retraining's 's (phase 2) test accuracies.
C HYPERPARAMETER TUNING: CROIS vs. GDRO
C. FURTHER HYPERAMETER EXPLORATION CROIS
In our paper, we have demonstrated CROIS's effectiveness even with just using the same hyperparam- eters as train an ERM model. In this section, we present results for further additional parameter tuning on the robust classifier retraining phase. These results provide empirical evidence for CROIS's potential as well as robustness different hyperparameter setting.
la regularization. We investigate whether additional regularization would be helpful to classifier retraining with GDRO on CelebA (Table 9) and Waterbird. (Table 10) We further examine the effects of regularization CivilComments (Table 11) to support our choice in Section A. For these
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Table 8: CROIS with GDRO (p 0.30) Waterbird. Average (Avg Acc) and Worst-group (Wg Acc) Accuracies for various epochs of the feature extractor ("Phase 1") and the corresponding test accuracies for classifier retraining ("Phase 2*). While training for longer epochs seem to help with average and worst-group accuracy for phase 2, the benefit is small. Hence, simply selecting the best validation average accuracy model, row Epoch 131 and denoted BEST here, yields good enough features that simplify our training procedure and model selection criteria.
CROIS (p 0.3)
Feature Extractor (Phase 1)
Classifier Retraining (Phase 2)
Phase
Epoch
Val Avg
Val WG
Val Avg
Val WG
Test Avg
Test WG
91.3
0.05
87.8
85.6
86.6
85.2
13.5
87.6
86
85.5
95.2
20.3
89.6
88
88.3
86.9
3
95.7
25.6
90.1
88
88.5
88.2
4
95.8
25.6
88.9
88.2
87.1
86.9
96.4
32.3
89.2
88.7
87.3
87.1
38.4
90.5
89.3
88.9
96.3
29.3
90.6
88.7
89.3
88.5
8
97.1
38.4
90.1
89.3
88
87.7
97.1
42.9
90.4
89.9
88.2
87.9
96.7
40.6
91.2
90.2
89.4
88
20
97.3
54,9
91.4
91
90.3
89.5
50
97.4
55
91.5
91
90,4
89.7
100
97.2
57.1
90.5
90.2
89.5
89.2
131 (Best)
97.6
55.6
90.9
90.2
89.7
89.6
150
97.2
60.2
90.7
90.2
89.6
89.4
200
97.5
52.6
91
90.2
88.8
88.2
250
97.2
59.4
91
90.4
88.3
87.7
experiments, further run each setting acorss random seeds to further investigate CROIS's sensitivity towards hyperparameters.
Table 9: Effects of & regularization on classifier retraining with GDRO on CelebA.
p = 0.50
p = 0.30
p = 0.10
f2 Reg.
Avg Acc
Wg Acc
Avg Acc
Wg Acc
Avg Acc
Wg Acc
91.6 (6.66)
87.4 (2.76)
90.9 (1.13)
88.4 (0.78)
91.2 (0.35)
90.0 (0.0)
10 2
92.1 (0.32)
87.6 (1.56)
91.8 (0.21)
87.5 (3.54)
92.0 (0.44)
88.3 (2.39)
10
91.9 (0.35)
88.2 (2.10)
91.3 (0.44)
90.6 (0.95)
91.3 (0.36)
90.3 (0.0)
0
92.2 (0.25)
86.8 (2.30)
91.5 (oo0)
87.8 (3.96)
92.1 (0.47)
88.1 1 (2729)
Table 10: Efffett of & regularization classifier retraining with GDRO Waterbird.
p 0.50
p 0.30
p 0.10
& Reg.
Avg Ace
Wg Acc
Avg Acc
Wg Acc
Avg Acc
Wg Acc
89.6 (0.3))
88.9 (1.37)
91.7 (0.4)
89.8 (0.68)
94.5 (0.00
85.8 (0:5)
10
90.4 (1.31)
89.3 (0.6))
90.6 (1.25)
89.2 (1.31)
95.1 (0.40)
86.3 (083)
10 -
90.4 (0:95)
89.5 (0.59)
90.8 (0:35)
89.6 (1.15)
95.4 (1.10)
83.5 (3244
89.8 (0.53)
89.3 (0.70)
90.6 (131
89.1 (1.16)
95.1 (0.35)
86.4 (050)
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Table 11: Effects of l2 regularization on classifier retraining with GDRO on CivilComments.
p
0.50
p = 0.30
p 0.10
2 Reg.
Avg Acc
Wg Ace
Avg Acc
Wg Acc
Avg Acc
Wg Acc
88.8 (1.30)
70.0 (1,63)
89.5 (0.35)
66.4 (1.99)
89.3 (1.69)
68.9 (2.44)
10 10-2
89.4 (0.9%)
70.6 (0.42)
89.5 (0.29)
68.5 (0.7)
88.6 (1.70)
70.2 (1.63
0
89.5 (0.70)
71.0 (1:50)
89.7
68.6 (1:53)
89.5 (1.81)
68.7 (1.72)
Learning rate. We examine the effects of different learning rates on CROIS on CelebA and Waterbird in Table 12. Lower learning rate seems to be more beneficial.
Table 12: Effects of varying learning rate on CROIS 0.30 on CelebA and Waterbird. We fix & regularization 10-4
Learning rate
10
10 44
10 33
10"
10 10-1
Average accuracy
92.2
91.4
91.2
90.1
91.2
CelebA
Worst-group accuracy
88.3
90
90.3
87.8
82.8
Average accuracy
89.7
90.6
94.2
93.2
93.9
Waterbird
Worst-group accuracy
89.5
88.9
87.1
88.8
78.5
C.2 SENSITIVITY TO MODEL CAPACITY: CROIS VERSUS GDRO
In this section, we present a comparison between CROIS and GDRO test performance with different l2 regularization configuration on CelebA and Waterbird in Table 13. On CelebA, GDRO is quite sensitive model capacity while it less on Waterbird. We note that GDRO fails converge to good stationary point when &g = 1 on CelebA (see Figure 3).
Table 13:
Worst-group test accuracy for GDRO and CROIS (p 0.30) with different & regularization.
CelebA
Waterbird
reg.
10-4
10-3
10-3
10-1
10-4
10-3
10-3
10
GDRO
81.7
81.7
81.7
83.9
87.8
0.00
86.8
86.8
86.8
86.8
87.1
86.5
CROIS 90.6
91.5
90.0
90.0
90.3
90.0
90.3
90.6
90.6
90.6
90.0
88.2
D FURTHER STUDIES ON CLASSIFIER RETRAINING
D.I IMPACT OF ROBUST RETRAINING INDEPENDENT SPLITS
In this section, we examine how robust retraining affects the model's prediction of Dy and Dr, before and after robust classifier retraining independent split (with p 0.30). Tables 14 and 15 show the accuracy on Du and D, for Waterbird and CelebA. The "Points changed" column indicates the number of points that the model changes prediction after robust retraining per group along with the total number examples in that group (with percentage in parentheses). The worst-group is underlined in the tables.
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Table 14: Model's prediction of Do and D on Waterbird before robust retraining and after robust classifier retraining on independent split.
Accuracy on Dy
Accuracy on DL
Waterbird (p = 0.3)
Before
After
Points changed
Before
After
Points changed
Avg Ace
100
94.5
184/3356 (5.48%)
96.4
89.6
162/1439 (11.3%)
Group 0 (73.0%)
100
92.6
180/2430 (7.41%) ))
99.6
88.2
122/1068 (11.4%)
Group I (3.84%)
100
99.3
1/141 (7.09%)
76.7
100
10/43 (23.3%)
Group (1.17%)
100
0/38 (0.00%)
44.4
100
10/18 (55.6%)
Group 3 (22.0%)
100
99.6
3/747 (0.40%)
90.8
92.3
20/310 (6.45%)
Table 15: Model's prediction of Dy and Dr on CelebA before and after robust classifier retraining on independent split. RR means after Robust Retraining.
Accuracy on Dy
Accuracy on DL
CelebA (p = 0.30)
Before
After
Points changed
Before
After
Points changed
Avg Acc
96.5
92.4
7537/113939 (6.61% ())
95.6
92.0
3274/48831 (11.3%)
Group (43.7%)
96.7
91.7
2609/50311 (5.19%)
95.8
91.3
1011/21318 (4.74%)
Group I (41.4%)
99.6
92.2
3489/46652 (7.48%)
99.6
92.2
1493/20222 (7.38%)
Group 2 (14.1%)
89.9
95.2
1000/16012 (6. (6.25%)
86.8
93.7
517/6868 (7.53%)
Group 3 (0.87%)
46.3
91.8
439/964 (45.4%)
35.5
95.3
253/423 (59.8%)
Interestingly, after robust retraining, the worst-group almost always switches from the minority group the majority group regardless of the data split.
D.2 THE IMPORTANCE OF MINORITY EXAMPLES: SUBSAMPLED RETRAINING
As alluded in Section 4.2, group-imbalance seems to play important role in the robust perfor- mance of CROIS. To further demonstrate this point, we consider how CROIS performs when the second phase is subsampled versus when it is allowed additional non-minority examples.
Table 16: Performance of CROIS when retraining is on a subsampled split versus the full split. Subsampling the split doesn't It seem impact CROIS's performance, indicating the importance of the availability of minority examples.
Subsampled retraining
Full retraining
Dataset (minority group size, fraction)
Avg Ace
Wg Ace
Avg Ace
Wg Ace
CelebA (n 423, 0.87%) Waterbird (n = 18, 1.2%)
90.6
89.7
91.9 90.3
88.9
91.3
87.6
88.9
As the result in Table 16 shows, there isn't 't significant difference between the two sampling strategies, suggesting that the availability of minority group examples plays important role in robust classifier retraining.
E ADDITIONAL COMPARISON
We provide additional baselines for comparison in the Tables below.
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Table 17: Experimental results for the setting when only group labels from the validation set are used. Results for C-DRO (CRaR DRO), LfF, EIIL. JTT and SSA are from Nam et al. (2022). Results for UMIX are from Han al. (2022). Results for CnC are from Zhang et (2022). The numbers in parentheses denote one standard deviation from the mean across 3 random seeds.
Waterbird
CelebA
MultiNLI
CivilComments
Method
Avg Ace
Wg Acc
Avg Acc
Wg Ace
Avg Ace
Wg Acc
Avg Ace
We Acc
C-DRO
96.0
75.9
82.5
64.4
82.0
68.0
92.5
60.5
LIF
91.2
78.0
85.1
77.2
80.8
70.2
92.5
58.8
EIIL
91.2
78.0
85.1
77.2
80.8
70.2
92.5
58.8
JTT
93.9
86.7
88.0
88.1
78.6
72.6
91.1
69.3
UMIX
93.0 (0.5)
90.0 (1.1)
90.1 (0.4)
85.3 (4.1)
N/A
N/A
90.6 (0.4)
70.1 (0.9)
CnC
90.9 (0.1)
88.5 (0.3)
89,9 (0.9)
88.8 (0.9)
N/A
N/A
81.7 (0.5)
68.9 (2.1)
SSA
92.2 (0.87)
89.0 (0:55)
92.8 (0.11)
89.8 (1.28)
79.9 (0.87)
76.6 (0.66)
88.2 (1.95
69.9 (2,02)
CROIS
92.1 (0.29)
90.9 (0.12)
91.6 (168)
88.5 (0.87)
81.4 (0.66)
77.4 (1.21)
90.6 (0.20)
70.3 (0.34)
Table 18: Additional comparison where group labels from the training set are available. Results for LISA taken from Yao et al. (2022). Results for CAMEL are taken from Goel et al. (2020).
Waterbird
CelebA
MultiNLI
CivilComments
Method
Avg Ace
Wg Ace
Avg Ace
Wg Acc
Avg Acc
Wg Ace
Avg Acc
Wg Acc
ERM
96.9
69.8
95.6
44.4
82.8
66.0
92.1
63.2
GDRO
93.2
86.0
91.81
88.31
81.4
77.7'
89.6 (0.23)
70.5 (2.10)
LISA
91.8 (0.3)
89.2 (0.6)
92.4 (0.4)
89.3 (..
N/A
N/A
89.2 (0.)
72.6 (0.))
CAMEL
90.9 (0.))
89.1 (0.4)
N/A
N/A
N/A
N/A
N/A
N/A
CROIS' P labeled fraction used for retraining (with 1 unlabeled fraction for the ERM phase)
0.10
95.4 (1110)
83.5 (3:24)
91.3 (0.36)
90.3 (082))
80.8 (0.51)
75.3 (2.06)
89.5 (L81)
68.7 (1.72)
0.30
90.8 (5)5)
89.6 (1.15)
91.3 (0.44)
90.6 (0.5)
80.0 (0.31)
77.9 (0.17)
89.7 (0.33)
68.6
E.I ADDITIONAL BASELINES FOR GROUP LABELS FROM ONLY THE VALIDATION SET
Baselines. In Table 17, we compare CROIS against JTT (Liu al., 2021) and SSA (Nam al., 2022), as well as additional baselines like CVaR DRO (Levy et al., 2020), LiF (Nam et al., 2020), EIII (Creager et al., 2021), CnC (Zhang et al., 2022) and UMIX (Han al., 2022). There, we report the mean and one standard deviation of the Test Average (Avg Acc) and Worst-Group Accuracy (Wg Acc) across three random seeds.
E.2 ADDITIONAL BASELINES FOR GROUP LABELS FROM TRAINING SET
In the setting where group labels are available from the training set, we compare our method against additional basclines like LISA (Yao al., 2022).
F FRACTION OF THE VALIDATION SET IMPLEMENTATION DETAILS FROM SECTION 4.1
Following the setup Section 4.1 and the setup as in Liu et al. (2021); Nam et al. (2022), we further reduce the validation set only small fraction, 5%. 10%, and 20%. We investigate CROIS's performance in this very few group-labels setting across CelebA and Waterbird in Section 4.1. We note that the highly reduced sample size poses new challenge and makes it harder simply reuse the default parameters.
Tuning l regularization: When using so little data, overfitting can become bigger problem, even when just training low-capacity linear classifier. Hence, we tune for higher values for l regularization across (10-1, 10-2, 10).
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Tuning learning rate: We also tune the learning rate across {10-t 1004, 1000 10 2) instead of simply reusing default parameters.
The use of group-labels and model selection: Since the amount of examples for classifier retraining is now significantly reduced, it might be wasteful to further split our available group- labels for validation. Instead, we use all the available group-labels for robust classifier retraining and perform model selection in the second phase via the train worst- group accuracy. The low capacity linear layer and higher &g regularization allows us to avoid overfitting when performing model selection this way. The feature extractor from the first phase is selected via the best average accuracy on the full group-unlabeled validation set.
sampling, a batch size greater than the number of examples in a certain group would cause duplicate sampling of the minority-gron group examples in the same step, artificially increasing the weight for that group. We further tune for batch sizes across grid of powers of 2 less than the smallest group or the default batch size (e.g. we search across {4, 8, 16} if the size the smallest group is 17).
Smaller batch size: Since GDRO requires
In Table 2, we present the results for CROIS with the above modifications and compare it to CROIS and JTT. There, robust retraining for CelebA is performed with an & regularization of 0.1, batch size of and learning rate 10-5, For Waterbird, we found that batch size weight decay 1, and learning rate 10-5 are best for 20% and 10% reduction. For 5% reduction, further reduce the batch size to 4 (since the minority group only has examples) and increase the weight decay to 10.
The results shows that CROIS maintains its robustness performance even at greatly reduced group- labels. This implies that even a few (minority) examples can help debias the final layer classifier with proper configurations.
G IMPLICATIONS AND TAKEAWAYS
Comparison to standard pretraining and finetuning As mentioned in the related works section as well as during the discussion of the motivation for our method, pretraining and then finetuning is a now well-known and established strategy in many domains. Our work differs the most significantly from this standard strategy through:
The use of independent splits: Traditional pretraining and finetuning reuses the dataset for both phase with the possibility of additional labels (contrastive learning, long-tail learning, etc.). We have demonstrated through extensive experiments the importance of independent split for classifier retraining to work well for the group robust setting.
2. The use of group robust algorithm for finetuning: We mainly utilize GDRO for our finetuning phase while most other works finetuning make use of simpler strategy for finetuning like reweighing or subsampling. We demonstrated in our experiments that GDRO yields the best robust performance over other more common methods.
Our work provides evidences that the features of ERM trained DNNs are rich enough solve the group-shift problem (when an abundant amount of group labels is available to retrain the classifier) and one of the major reasons for poor worst-group performance of an ERM trained DNN within its classifier layer. We then further demonstrate that even few group labels can sufficiently "fix" the classifier to achieve better group-robust performance.
This knowledge can potentially be useful towards solving the seemingly much harder group-agnostic setting (where no group label available) by allowing the practitioner to focus on obtaining a robust classifier given an ERM trained feature extractor. Our experiments further show that reasonable robustness can be achieved with relatively few group-labels (that are not used to obtain the feature extractor), which makes the problem seem closer in reach.
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Reviews:
[{"review_id": "mdmvCHAVoX", "reviewer": "Reviewer_X9ti", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes a simple method called CROIS for learning a model that can be applied in group-shift settings, i.e. cases where the performance of the group that is underrepresented in the training set is low when there is a distribution shift.\nCROIS is motivated by the idea that although ERM-trained DNNs often take advantage of spurious features, previous works show its capability to produce good features.\nCROIS consists of 2 steps method 1) train an ERM model on data without the group label information and 2) correct the classifier layer by using the features extracted from stage 1) to train a GDRO model on the data with group labels. Although in practice in the experiments the hyperparameters are fixed, for both steps the model selection can be done on the validation set, the first one optimising the aggregate accuracy and the second one the worst-group accuracy.\n", "summary_of_the_review": "The idea is interesting and promising, but in the current state, I am not very convinced the work is ready to be published. The authors should revise their experiments section to address my doubts. \n\n", "strength_and_weaknesses": "**Strength**: \n\nThe paper is well written, with simple and relevant notation. CROIS requires fewer labelled data than the GDRO baseline considered, and it does not rely on pseudo-labelling as JTT. The authors perform a good ablation study on different components of their proposed method (e.g. sampling with or without replacement, i.e. using independent splits or not, first and second stage different training strategies, robustness towards the hyperparameters tuning).\n\n**Weaknesses**: \n\nFully training group labels baselines could include more recent methods such as SGDRO (non-flat version of GDRO proposed in Goel et al.). \nThere is a misleading sentence on page 3 when describing the Waterbirds dataset: “The bird images are then modified with either a water or land background.” There is no “modification” involved, in the dataset the birds are already placed either on water or land background (this is a minor note, but should be revisited).\nThe main weaknesses are all in the “Experiments” section. 1) Although the hyperparameters are fixed, some ablation is still required (for example the regularisation in the second stage for the CivilComments dataset). 2) In Tables 1 and 2 it is not clear what the results are, is it correct to assume they are the mean and std. performances over three different initialization seeds? 3) The results in Tables 1 and 2 are not correct, CROIS is using the validation set for training in the second stage, not only for model selection. For the datasets considered, the validation set has the same distribution as the test set, it does not seem correct to talk about “group shift” anymore. 4) Table 2 should also include the average test accuracy to show the trade-off between average and worst-case performances when varying the size of the validation set. Also, why is the std. not reported here? 5) Table 3 shows promising results, but the best fraction “p” is a hyperparameter that should be tuned using the worst-group performance on the validation set, like the regularization term for GDRO.  Its behaviour is not linear (e.g. the more the better) nor consistent across datasets. For example, in both text datasets, only one fraction produces better results than the GDRO baseline. Here, I disagree with the statement “In practice, p is not a parameter to choose (there’s no reason to throw away group labels) but rather is limited by the resources available to obtain group labels”: using p=0.5 is often worse than using p=0.3. 6) To obtain robust results, it would have been better to evaluate the methods across different splits of train-val-test, not simply different initialisation seeds. \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "0J2qcFNMVnW", "reviewer": "Reviewer_ysYp", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a very simple method termed classifier retraining on independent splits to improve the subpopulation shift robustness of the model. The main algorithm of the paper is clearly shown in Algorithm 1 of the paper.", "summary_of_the_review": "This paper proposes a very simple and effective method. But some experiments are unfair.", "strength_and_weaknesses": "Advantages.\n\nThis paper is well written and easy to follow. Although the method of the paper is too straightforward, the author explains its principle very clearly.\n\nWeaknesses.\n\nHowever, I still have the following concerns: \n1. The comparison experiments in Table 1 is unfair. Since the proposed method uses the data from the validation set for training.\n2. The paper does not conduct discussion and compare with state-of-the-art methods, e.g., LISA[1], UMIX[2], CnC[3], CGD[4].\n\n[1] Improving out-of-distribution robustness via selective augmentation.\n\n[2] UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup\n\n[3] Correct-n-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations\n\n[4] Improving out-of-distribution robustness via selective augmentation.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "0C06gc7jCBG", "reviewer": "Reviewer_2dWG", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors proposed a novel approach to improve worst-group performance, CROIS, that works well with reduced/limited group annotations on training/val set. The idea is simple yet effective: obtain good feature extractors on group-unlabeled data with ERM loss, and only retrain the last layer on group-labeled data with GDRO loss. Empirical experiments and abundant ablations are performed to support the method.", "summary_of_the_review": "Though the empirical results do not outperform SOTA, the paper still presents a novel and interesting idea in worst-group improvement that is quite different from usual approaches in this area. A key question regarding the group-labeled size for classifier retraining can be better explained for other researchers to build upon the work and/or practitioners to apply the proposed method.", "strength_and_weaknesses": "Strengths:\n\nThe proposed method is a simple, novel and interesting idea that is orthogonal to the usual pseudo-group-labeling approach taken by other existing methods like JTT.\n\nWeaknesses/comments:\n\n1. The relationship of the CROIS performance to the data size for retraining (group-labeled fraction in training set for CR - Table 3, or reduced val size for CR - Table 2) seems quite inconclusive, as also pointed out by the authors in Sec 4.2 (Page 8). In particular when one cross compares Table 2 and 3, I assume for instance 30% of training size (Table 3) is comparable to 100% of val size (Table 2) for CelebA, which happens to be the sweet CR size for this dataset? IMHO p is the (only) key parameter in Algorithm 1, and it would be good to clarify in the paper, for instance, what's the rule of thumb of choosing p? Would it make sense to consider the CR data size (or p) as a tunable hparam? If group-unlabeled size >> group-labeled size, then should one use 50/50 group-labeled in D_L/D_val or tuning the ratio is still needed?\n2. Empirical results are somewhat strong, on par with (but not significantly outperforming) SOTA (e.g. Table 1). Many other baselines (Sec 1.1) should be added to the results (e.g. Table 1) for comparison. Besides, it would be good to include error bars in Table 2 to show that error bar should become much larger when val size becomes smaller despite the \"relatively small\" drop in mean performance.\n3. In Sec 3, the authors claim that independent splits are done to avoid the feature extractors memorizing spurious correlations with class labels. But many modern DL models rely on unsupervised/contrastive/label-free training of feature extractors. For instance, one can continue the pretraining of BERT on the in-domain data on the two language tasks (https://arxiv.org/abs/2004.10964). Perhaps then independent splits still won't be required and more group labels can be used for classifier retraining (in case of limited group annotations)?\n4. The abstract/intro clearly separates the two use cases: a) full training and b) val group annotation. Algorithm 1 (Sec 3) does not make such distinction where D_L is listed as an input. It only becomes clear how Alg 1 is applied to case b) in Sec 4.1 where val set is actually split 50/50 into D_L and D_val. For consistency, could consider clarifying this earlier in the paper to avoid confusion.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject