Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation 
Authors: Junhyun Nam
Jaehyung Kim
Jaeho Lee
Jinwoo Shin
Venue: ICLR 2022 Poster (2022)
Keywords: worst-group loss minimization
spurious correlation

Abstract: The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works focus on developing weaker forms of supervision---e.g., hyperparameters discovered with a small number of validation samples with spurious attribute annotation---but none of the methods retain comparable performance to methods using full supervision on the spurious attribute. In this paper, instead of searching for weaker supervisions, we ask: Given access to a fixed number of samples with spurious attribute annotations, what is the best achievable worst-group loss if we ''fully exploit'' them? To this end, we propose a pseudo-attribute-based algorithm, coined Spread Spurious Attribute (SSA), for improving the worst-group accuracy. In particular, we leverage samples both with and without spurious attribute annotations to train a model to predict the spurious attribute, then use the pseudo-attribute predicted by the trained model as supervision on the spurious attribute to train a new robust model having minimal worst-group loss. Our experiments on various benchmark datasets show that our algorithm consistently outperforms the baseline methods using the same number of validation samples with spurious attribute annotations. We also demonstrate that the proposed SSA can achieve comparable performances to methods using full (100%) spurious attribute supervision, by using a much smaller number of annotated samples---from 0.6% and up to 1.5%, depending on the dataset.

Published as a conference paper at ICLR 2022
SPREAD SPURIOUS ATTRIBUTE: IMPROVING WORST-GROUP ACCURACY WITH SPURI- OUS ATTRIBUTE ESTIMATION
Junhyun Nam', Jachyung Kim', Jacho Lee² Jinwoo Shin'
KAIST, 2POSTECH {junhyun. nam, jaehyungkim, hitp::/nnnniiiiiiosssooooooo .c.kr jaeho. Leefpostech..c..kt
ABSTRACT
The paradigm of worst-group loss minimization has shown its promise in avoid- ing to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works focus on developing weaker forms of supervision-e.g. hyperparameters discovered with small number of group-labeled samples with spurious attribute annotation but none of the meth- ods retain comparable performance to methods using full supervision on the spu- rious attribute. this paper, instead of searching for weaker supervisions, we ask: Given access to a fixed number of group-labeled samples, what is the best achievable worst-grout loss if we "fully exploit" them? To this end, we propose secuud--ttribute-bee
algorithm, coined Spread Spurious Attribute (SSA), for improving the worst-group accuracy. In particular, we leverage samples both with and without spurious attribute annotations train model predicting the spuri- ous attribute, then use the pseudo-attribute predicted by the trained model as a supervision on the spurious attribute train a new robust model having minimal worst-group loss. Our experiments on various benchmark datasets show that our algorithm consistently outperforms the baseline methods using the same number of grbueelabeled samples. We also demonstrate that the proposed SSA can achieve comparable performances to methods using full (100%) spurious attribute super- vision, by using a much smaller number of group-labeled cimples-from 0.6% and up to 1.5%, depending on the dataset.
INTRODUCTION
Machine learning models trained on datasets containing spurious correlation (also known as "short- cuts") often end up learning such shortcuts instead of intended solutions (Geirhos et al., 2020). For example, consider an image classification dataset of 'cows' and 'camels,' in which most images of cows appear on grasslands and camels on deserts. When trained on such a dataset, models often learn to make predictions based on the landscape instead of the object (Beery et al., 2018). This phenomenon can lead to very low test accuracies on groups underrepresented in the training set.
Various approaches have been proposed to resolve this gap, and the idea of minimizing the worst- group loss loss-e.g.. Sagawa et al. (2020)-has arisen as one of the most promising solutions. This approach forces high performances on both the majority group (e.g., cows standing on grasslands) and the minority group which contradicts the spurious correlation (e.g., cows standing on deserts). Despite its effectiveness, the worst-group loss minimization approach has a drawback: The learner requires supervision on which group each training sample belongs. For example, the learner needs to know that sample belongs to both the 'cow" and the 'desert' categories to utilize such group infor- mation perform worst-group loss minimization. Even if put aside the issue of identifying the spuriously correlated attributes ('desert' and 'grass') in the first place, one still needs to collect addi- tional annotation on such spurious attributes. Acquiring such fine- fine-grained annotation presumably more expensive to collect, as the annotator needs a clear understanding of both the target attribute ('cow' and 'camel') and the spurious attribute (' ('desert' and 'grass').
Work done KAIST

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Acknowledging this difficulty, recent works propose worst-group loss minimization algorithms that require a smaller number of group-labeled training samples (i.e.. training samples with spurious attribute annotations) (Nam et al., 2020; Liu et al., 2021). At a high level, these works share simi- lar strategy (Fig. 1, Left):: The methods first use a specialized mechanism to identify minority group samples among grouppunlabelle sis on identified-as-mie minority
training samples, and train model in way that puts more empha- miniority samples, c.g., by upweighting. A small set of group-labeled samples are used tune hyperparameters of this procedure; Liu al. (2021) shows, the performance of trained models are very sensitive to these hyperparameters, indicating the high dependency of such algorithms on the availability of the group-labeled samples. Although these methods achieve higher worst-group accuracy than completely annotation-free approaches, e.g., Sohoni et al. (2020), they fail to perform comparably to the algorithms which use full annotations on spurious attributes, e.g., Sagawa et al. (2020). This performance gap gives rise to the following question: Can we closely achieve the full-annotation performance using a partially annotated dataset, if we use group-labeled samples more actively than hyperparameter-tuning
Contribution. This paper proposes a worst-group loss minimization algorithm, coined Spread Spu- rious Attribute (SSA). At high level, SSA consists of two phases-psendlee peeeess-ssooo-bblliii and robust training-thar are designed to make a full use out of the group-labeled samples (Fig. 1, Right):
Pseudo-labeling SSA trains spurious attribute predictor, using both group-labeled and group- unlabeled samples (i.e., samples lacking spurious attribute annotations). We find that group im- balances underlying the dataset which can be detrimental to the performance (as in Kim et al. (2020)), and propose using group- wise adaptive thresholds to mitigate this problem. The predictions are used as pseudo-labels (Lee, 2013) on the group-unlabeled samples.
render pseudo-labels to be biased towards the majority group,
Robust training: Based on the pseudo-labede training set, SSA trains model which predicts the target attribute, using the worst-group loss minimization algorithms developed for fully supervised cases. We use Group DRO (Sagawa et al., 2020) as default choice, and re-use group-labeled samples for the hyperparameter tuning.
Our experimental results suggest that SSA is general yet effective framework for worst- group loss minimization. On various benchmark datasets, SSA consistently achieves performances comparable to fully supervised Group DRO and outperforms other baseline methods even when using only 5% of the group-laheled samples compared to the baselines. To be more specific, SSA requires less than 1.5% (as little as 0.6%) of group-labeled samples- 993 out of 182637 on CelebA, and 4123 out of 288637 on MultiLLI-to achieve a worst-group accuracy similar to that of 100% usage. Moreover, we find that such benefits of SSA persist when combined with other robust training methods, such as N-rrect--CConrast (CNC); Zhang et al. (2021)), the supervised contrastive learning with contrastive batch sampling using group information. Finally, we also empirically observe that the group-wist adaptive thresholding technique wwhch we proposed for better pseudo-labeling- saaudd-labllnngeejjoy enjoo a broader usage for addressing the general problem of semi-supervised learning under class imbalance; exist- ing pseudo-labeling techniques (Kim et al., 2020; Wei et al., 2021) require certain assumptions in datasets to work well for the task, while ours does not.
2 RELATED WORKS
Improving worst-group accuracy with group annotations. It has been known in various literature that machine learning models often perform significantly worse the samples from groups with a relatively small number of training samples than on samples from the majority group. The class imbalance problem (Japkowicz, 2000; Johnson & Khoshgoftaar, 2019) is one of the representative cases of this phenomenon. Here, class labels naturally define group identities and thus do not re- quire any additional group annotations. In this context, popular strategies to improve worst-group performances are using re-weighted loss for each group (Huang et al., 2016; Khan et al., 201 7) and re-sampling the given dataset to balance the group distribution of the training dataset (Chawla al., 2002; He& & Garcia, 2009). This paper, in contrast, considers setup where the group is defined as a combination of the label (target attribute) and the spuriously correlated attribute; unlike the class imbalance literature, the availability of the full group identity information is not guaranteed,
We use the term CNC to refer to its supervised contrastive learning procedure with contrastive hatch sam- pling only, rather than the entire process which includes spurious attribute annotation estimation via ERM.
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Objective: classify O vs.
Obstacle: color and shape are highly correlated (most O are O are ) no color annotation the training set
group-urlabeled set
identify samplee to be upweighted
group-unisbeled set
obtain peeudo spurious attribute
labeled
group-aabeted set
train
validale
Comparison of prior approaches (left) and the proposed SSA (right). Left: Prior works use group-labeled samples only for hyperparameter tuning. Right: The proposed SSA uses group- labeled samples for both training and validation of the model.
Figure 1:
as spurious attributes may not have proper annotations. Under the setting where spurious attribute annotations are available all samples, Sagawa et al. (2020) gives an online optimization algorithm that shows promising results on minimizing the worst-group loss.
To reduce additional annotation costs, recent works aim to train a robust model without requiring group annotations for all training samples. These works utilize models trained with standard procedure identify samples that dis- agree with spurious correlations. Nam et al. (2020) train a model to be intentionally biased using the generalized cross entropy loss, and use it to ing another model. Liu et al. (2021) train a standard ERM model for a few epochs and upweight samples misclassified by this model train the second model. Although these approaches do not use any group information for training, they still use small number of group-labeled samples for hyperparameter tuning, which is critical to their worst-group performance. In this paper, we design method to utilize group-labeled samples more efficiently. We note that, in the class imbalance lit- erature, another line of works proposes to use croup-labeled samples more actively; they use labeled samples for determining sample weights for the training set through meta-learning (Ren et al., 201 8) or training model to predict sample weights (Shu et al., 2019).
Improving worst-group accuracy without group annotations.
high-loss samples for train-
PROBLEM SETUP
Consider the learning a classifier in the presence of spurious correlations in the training set. Fol- lowing the prior work of Sagawa et al. (2020), we cast this problem as minimizing the worst-group loss, where the group identity is determined by the target attribute (that we want to predict) and the spurious attribute (that want to ignore). We assume that do not have spurious attribute annotations of the samples the training set ggroup-unlabelde set), but have an access to additional of samples with both spurioust attribute and target attribute annotations (group-labeled set).
More formally, we let each sample be a triplet consisting of an inpul x E X, a larget attribute y y, and spurious alfribute a E A. Our goal is train parameterized model fo X - y that minimizes the worst-grous grss- expected loss on test samples; for the purpose of avoiding learning spurious correlations, we define the group as an attribute pair g == (y. a) y x A= G. other words, we aim to minimize
R(0) max E. P; 9Eg
for some loss function f: )/ x y TR where Pg denotes the group-conditioned data-generating distribution. Note that, focus on the group defined by the attribute pairs, P. is deterministic on y and a, and stochastic only on x.
We assume that the learner has access to two types of samples for training: The group-unlabeled set consists of n samples without spurious attribute annotations, i.e., Do := {(x1 ((z1.81)... yı)..... (In, Vn } and the group-labeled set consists of samples with spurious attribute annotations, i..., Dz { 31, ā,), ...,, (īm, Ins ām)}. We assume that the group-labeled samples are much more costly collect, and thus have n m.
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4 SPREAD SPURIOUS ATTRIBUTE
We now describe the algorithm we propose, Spread Spurious Attribute (SSA). At a high level, SSA consists of two phases: pseudo-labeling and robust training. In the pseudo-labeling phase, SSA trains a model to predict the spurious attribute. In particular, we use both group-labeled and group- unlabeled samples to generate pseudo-labels on the group-unlabeled training samples (Section 1, with an adaptive thresholding technique for balancing the number of samples in each group (Sec- tion 4.2). Then, in the robust training phase, SSA uses generated pseudo-labels to train a model that predicts the target attribute with a small grost loss (Section 4.3). We use Group DRO (Sagawa et al., 2020) as our default robust training method, but SSA also performs well when combined with other existing algorithms that use full spurious label supervisions (see Section 5.3).
4.1 PSFUDO-LABRLING USING GROUP-LABELED AND GROUP-UNLABELER SAMPLES
Using both group-labeled set D and group-unlabeled set Dy. we train a model to predict spurious attributes on the group- unlabeled samples. The predictions will be used to generate artificial spuri- ous attribute labels on the group-unlabeled training samples, called pseudo-labels (Lee, 2013). We emphasize that we also use Dy for training the model, instead of training solely based Dz. In fact, as we shall see in Section 5.2, the additional use of training set brings significant performance boost. More specifically, we first partition both the group-labeled and group-unlabeled set into two:
D,. = Dj, UD, DU = Di DJ.
(2)
We use Di, Di to train the spurious attribute predictor that make prediction Di, and validate the model with Di.² To train this predictor, we use loss function consisting of two terms: the supervised loss for the samples in Di, and the unsupervised loss for samples in Dj. The supervised loss is simply the standard cross entropy loss. For the unsupervised loss, we use the cross entropy loss between the prediction and the pseudo-labels, i.e. labels generated by taking the arg max of predictions. Following prior works (e.g.. Sohn et al. (2020)), we apply the loss only if the confidence of the prediction exceeds some threshold 7 2 0. More formally, let us denote the class probability estimate of the predictor on the attribute a given the input x as p(a(zr), and the pseudo-label from this prediction as ā(x:) = arg max A (() Then, the supervised and unsupervised losses are
( y, a) = (E(((-1x), a). Lassup(x, y) 1 maxp(alx) 2 r} CE CE (p(+1zz), a(x)), (3)
aeAA
where CE(((-Ir), a) denotes the cross-entropy loss between the prediction 50- (z) and label a. The total loss is then given as the sum of the supervised and unsupervised loss
L = Epg, [&aup y, a)] + Epg. [ y)]
(4)
As we will describe in Section 4.2, additionally group-wise adaptive thresholds for pscudo- labeling (i.c., set different 7 for each group). The thresholds are determined in a way that balances the uveudo-group (i.e., the pair 9 (y, o(z))) population of the samples with confidence exceeding the group-wise threshold. This strategy helps the model to avoid making a pseudo-label prediction biased toward the majority group. Also, note that make predictions only samples in Di and not on samples in Di; empirically, we observe that this "splitting" of group-unlabeled samples is beneficial for performance comparing using the whole Drr for training (at the cost of running pseudo-labeling multiple times). For more discussions and ablation studies, see Appendix A.2.
4.2 BALANCING GENERATED PSEUDO-LABELS VIA ADAPTIVE TURESHOLDS
As we train the spurious attribute predictor with the loss (Eq. (4)), the prediction confidence of the model on each sample gradually increases. Ideally, we want the number of highly-confident samples (i.e., with confidence greater than 7) to increase uniformly over all pseudo-groups so that each greudo-rouup contributes evenly to weight updates. However, if the underlying group population is severely imbalanced as is common with spurious attributes- samples from the majority group often attains high prediction confidence significantly faster than samples from minority groups, by receiving more frequent gradient updates. This training imbalance leads to a severer imbalance in the pseudo-labels, resulting in detrimental effects on the downstream training.
We write to denote that samples are "visible" during the training phase, and to denote that they are not.
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To mitigate this majority group bias, set different thresholds for each pseudo-group, group, that the
same number of samples from each group is used for training with Eq. (4). To do this, we first set fixed threshold Timin for the group with the smallest population in the training split of the group-laheled set, i.e., the group defined as
Omin arg min (g)
where Di(g)= = (I, 9
(5)
gεyxA
Next, we count the number of samples in Di which (a) belongs Imin after pseudo-labeling, and (b) the prediction confidence exceeds Tgmin Then, we set thresholds for other groups to have same number of samples from each group. Concretely, let Di (g. Tg) ) be the set of samples in the training split of the group-unlabeled set with pseudo-group y and confidence greater than equal to Tg. i.e.,
Di (g. Tg) == ((r, v) E Di g = a(x)), max p(a (z) 2 Tg)
aEA
Then, for each group g # Imin. set Ty to be the smallest real number such that
[D) (g. Ty)l [Di (9min) + [Diginnn Tymsn) )
With this group-wise adaptive threshold, use the unsupervised loss revised
Cansup (z, y) = { max p(alz) 2 T(y.ar))) CE (f(-1z), a(x))
(8)
aE
We show the effectiveness of this group-wise adaptive threshold in Section 5.2, and further applica-
bility to class-imbalance problem Section 5.4.
4.3 WORST-GROUP LOSS MINIMIZATION WITH ESTIMATED PSEUDO-GROUPS
After training the model using the revised loss (Section 4.2), we generate final pseudo-labels for all samples in the training set. In other words, we generate
Du = (x, (
(I,z) Du).
(9)
We put pseudo-labels on all samples without applying any threshold, which allows us utilize the whole training set. In the robust training phase, we use the pseudo-labeled dataset Dy and the group-labeled dataset Dr to perform worst-group loss minimization. We use Group DRO (Sagawa et al., 2020) our default algorithm, using Dú for training and D2 for validation. We note that SSA can also use other robust training subroutines instead of group DRO; in Section 5.3, we show that our framework performs well when combined with CNC (Zhang et al., 2021).
5 EXPERIMENTS
Here, we briefly describe the experiment setup that will be used throughout all experiments in this section, except for Section 5.4 where we consider a slightly different scenario.
Datasets.
We evaluate SSA on two image classification datasets (Waterbirds, CelebA) and two natural language processing datasets (MultiNLI, CivilComments. WILDS)) containing spurious cor- relations. For all datasets, we use the validation split of the dataset as the group-labeled set. Below, we briefly describe each dataset and the corresponding spurious correlations.
Waterbirds (Sagawa et al., 2020): Waterbirds is an artificial dataset generated by combining bird photographs in the Caltech-UCSD Birds dataset (Wah et al., 2011) with landscapes from Places (Zhou et al., 2017). The goal is to classify the target attributes y {waterbird, landbird] given the spurious correlations with the background landscape A = [water background, land background).
CelebA (Liu et al., 2015): CelebA dataset consists of the face pictures of celebrities, with various annotations on facial//emooraphii features. We use the hair color as the target attribute y (blond, non non-blond), given the spurious correlations with the gender A = [male, female].
MultiNLI (Williams et al., 2018): MultiNL is multi-genre natural language corpus where each data instance consists of two sentences and a label indicating whether the second sentence is entailed by, contradicts, neutral to the first. We this label the target attribute (i.e., (entailed, neutral, contradictory }). and use the existence of the negating words as the spurious attribute (i.e., (negation, negation)).
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Table 1: Average and worst-group test accuracies evaluated on image classification datasets (Water- birds, CelebA). For more rigorous comparison, we run SSA and Group DRO on 3 random seeds and report the average and the standard deviation. Results of ERM, CVaR DRO, LfF and JTT are from Liu et al. (2021). Results of EIIL on Waterbirds are from Creager et al. (2021). Best performances (among methods using only validation set labels) are marked in bold.
Amount of group label used
Waterbirds
CelebA
Method
Avg.
Worst-group
Avg.
Worst-group
ERM
val. set
97.3
72.6
95.6
47.2
CVaR DRO (Levy et al,, 2020)
val. set
96.0
75.9
82.5
64.4
LfF' (Nam et al., 2020)
val. set
91.2
78.0
85.1
77.2
EIIL (Creager el al., 2021)
val. set
96.9
78.7
91.9
83.3
JTT (Liu al., 2021)
val. set
93.3
86.7
val, set
92.2
89.0 19.0
92.8gmm
89.8
SSA (Ours)
of val. set
92.61aua
87.1
92.8
86.7.11
Group DRO (Sagawa et al., 2020)
train. & val. set
91.8+048
89.2-a.8
93.1+021
88.5+111
Table 2: Average and worst-group test accuracies evaluated on natural language datasets (MultiNLI, CivilComments- WILDS). For more rigorous comparison, we run SSA and Group DRO on 3 random seeds and report the average and the standard deviation. Results of ERM, CVaR DRO, LIF and JTT are from Liu et al. (2021). Results of EIIL on CivilComments are from Creager et al. (2021). Best performances (among methods using only validation set labels) are marked in bold.
Amount
MultiNLI
CivilComments WILDS
Method
group label used
Avg.
Worst-group
Avg.
Worst-group
val. set
82.4
67.9
92.6
57.4
CVaR DRO (Levy 2020)
val.
82.0
68.0
92.5
60.5
LfF (Nam et al., 2020)
val. set
80.8
70.2
92.5
58.8
EIIL (Creager et al., 2021)
val. set
79.4
70,9
90.5
67.0
JTT (Liu et al., 2021)
val. set
78.6
72.6
91.1
69.3
val. set
79.9
76.6
88.2
69.9:2i2
SSA (Ours)
5% of val. set
80.4
76.5+1.9
=
69.5
Group DRO (Sagawa et al., 2020)
train. & val.
81.4+1.
76.6+041
87.7+135
69.1+1.3
CivilComments-We WILDS (Borkan et al., 2019; Koh et al., 2021): CivilComments- WILLDS con- sists of comments generated by online users, each of which are labeled with the toxicity in- dicator y {toxic, non-toxic). We use demographic attributes of the mentioned identity A {male, female, White, Black, LGBTQ, Muslim, Christian, other religion} as spurious at- tribute for evaluation purpose. We note that a comment can contain multiple such identities, so that groups defined by )' x A can be overlapped. Therefore, use A' (any identity, identity) as a spurious attribute for training, following Liu et al. (2021).
Models. For the all experiments image classification datasets, starting from maageNet-pretraingg BERT (Devlin al., 2019). We use the same architecture for predicting the spurious attribute (in the lseudo-labeling phase) and the target attribute (in the robust training phase).
ResNer-50 (He al., 2016) weights. For experiments on language datasets, we use pretrained
5.1 MAIN RESULTS
We compare the average and worst-case performance of the proposed SSA against the standard empirical risk minimization (ERM) and recent methods that tackles spurious correlation without group annotation for the training, including CVaR DRO (Levy et al., 2020), LfF (Nam et al., 2020), EIIL (Creager et al., 2021), JTT (Liu et al., 2021), and Group DRO (Sagawa et al., 2020) requiring group annotation to minimize the worst-group loss.
In Table 1, 2, we report average accuracies and the worst-group accuracies on all datasets we con- sider. Our method consistently outperforms all the other approaches that use spurious attribute annotated dataset for validation while using the same amount of spurious attribute annotation. No- tably, our method shows comparable performance to Group DRO which uses full amount of spurious attribute annotation for the training set, even outperforms CelebA dataset. We also our algo-

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Table 3: Worst-group accuracy on Waterbirds and CelebA with varying group-labeled set size. Re- sults of JTT are from Liu et al. (2021).
Waterbirds
CelebA
Method
100%
20%
10%
5%
100%
20%
10%
5%
JTT (Liu et al., 2021)
86.7
84.0
86.9
76.0
81.1
81.1
81.1
82.2
SSA (Ours)
89.0
88.9
88.9
87.1
89.8
88.9
90.0
86.7
Table Group-wise accuracy (recall) spurious attribute predictor trained in the pseudo-labeling phase, on the CelebA dataset. Worst-group accuracies marked in bold.
Amount of group label used
# (Blond, Male)
Non-hlond
Blond
Method
Dz.
Female
Male
Female
Male
Vanilla
90.2
90.3
97.0
70.4
Pseudo-labeling
10% of val. set
18
94.2
95.5
98.8
76.4
Group wise threshold
89.5
93.9
95.1
83.6
Vanilla
88.0
89.7
96.3
68.7
Pseudo-labeling
5% of val. set
8
92.2
94.2
98.4
67.8
+ Group-wisc threshold
83.3
90.6
92.7
75.7
rithm using only 5% of the default validation set to show efficiency of our algorithm to improve the worst-group accuracy. We further provide analysis on varying group-labeled set size below.
Effect of group-labeled set size. Although we focus on improving the worst-group performance with given amount of spurious annotation, reducing the amount of supervision is an important topic to discuss especially with high annotation cost. the main results Table I, 2, we use the default validation sets provided by each dataset as D2. To further test whether SSA can achieve high worst- group accuracy with reduced amount of supervision, we run our algorithm with small fraction of the default validation sets as group-labeled sets. Following Liu et al. (2021), run our method using 100%, 20%, 10%, 5% of the default validation set. In Table 3, we report the worst-group accuracy of JTT and our algorithm we find that our method maintains high worst-group accuracy even with the 10% of the original validation set. Most notably, the number of attribute annotated samples used for training spurious attribute predictor is 58 in total, 6 for the worst-group in Waterbirds when we only use 10% of the default validation set.
Waterbirds and CelebA, with various group-labeled set size. Surprisingly,
5.2 DETAILED ANALYSIS ON THE PSEUDO-LABELING PHASE OF SSA
We now take closer look at the labelin phase of the proposed SSA. In particular, we focus on validating the effectiveness of the group-wise adaptive threshold we introduced in Section 4.2. The purpose of the adaptive threshold was to prevent the pseudo-labels from being biased towards the majority group during the pseudo-labeling phase; the prediction confidence of the majority group samples may exceed the threshold faster than samples of minority group, increasing the contribution of majority-gron ggroup samples on the loss even further. In the first set of experiments (Table 4), we perform ablation studies on the pseudo-labeling and the adaptive threshold to see their effects on the spurious attribute prediction performance of the SSA. In the second of experiments (Table 5), we validate if the pseudo-label are biased towards the majority group and check that our adaptive threshold strategy successfully addresses the phenomenon.
Accuracy of the spurious attribute predictor. In Table 4, we report the group-wise spurious at- tribute prediction accuracies of SSA on CelebA dataset (using 5% or 10% of the validation split as the group-labeled set) in pseudo-labeling phase for following ablations: (1) Vanilla: Does not use any laeudo-labels and train the model using only the validation set samples. (2) Psendo-labeling: Uses pseudo-labeling with fixed threshold, and (3) +Group-wise threshold: Identical to SSA, us- ing group- wise adaptive thresholds for aseudo-labeling We find that using the group-wise threshold increases the spurious attribute prediction accuracy for the worst-group-ff (y. a) (Blond, Male), providing 7% boost in both cases. Interestingly, we observe that pseudo-labeling with fixed thresh- olds can even slightly degrade the performance, when the validation set is too small (5%).
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Table 5: Samples statistics during the training of spurious attribute predictor on CelebA with 5% of the default validation set.
Non- blond
Blond
Fraction of (Blond, Male)
Description
Female
Male
Female
Male
Number of group-labeled samples ((Pi))
213 47750
206
71
4
0.81%
Number of group-unlabeled samples (P&D
44617
15240
906
0.83%
Pseudo-labeling
Number of samples exceeding lixed threshold
45897
43201
15344
712
0.68%
Number of samples used for training
45897
43201
15344
712
0.68%
Group wise threshold
Number of samples exceeding fixed threshold
32926
34622
12733
712
0.88%
Number of samples used for training
501
528
681
716
29.5%
Table 6: Average and the grost accuracy of the proposed SSA algorithm, when combined
with two different choices of supervised worst-case loss minimization algorithms. The numbers in brackets are the gap closed by SSA between ERM and supervised robust training methods.
Waterbirds
CelehA
Robust training method
Group label
Avg.
Worst-group
Avg.
Worst-group
ERM
97.3
72.6
95.6
47.2
Ground truth SSA (Ours)
91.8=0.4
89.2+aas
88.5
Group DRO (Sagawa et al., 2020)
92.222077
89.0ae55
92.8
89,8
Gap closed by SSA
(98.8%)
(100%)
Ground truth SSA (Ours)
92.5
90. 00.0=
92.5+
87.4+aas
CNC (Zhang et al., 2021)
92.62m.0
89 29.22.224
91.7
87. 87.62003
Gap closed by SSA
(95,4%)
(100%)
Population comparison. In Table 5, we compare the populations of pseudo-labeled samples that contribute to the training, for pseudo-labeling using a fixed threshold ("Pseudo-labelinn" and the adaptive threshold ('+Group-wise threshold'); ); for each method, trained the spurious attribute predictor until the worst-group spurious attribute prediction accuracy reaches the highest point. We used CelebA dataset with only 5% of the validation split. From the experimental results, we find that naïve pseudo-labeling with a fixed threshold indeed leads the pseudo-labels to be biased towards the majority group using only 0.68% of the minority group (male blond) for training while the true fraction of the group is over 0.8%. On the other hand, using the adaptive threshold successfully addresses this problem, lifting the fraction of blond male samples to 0.88%, which is close to the population level. More impressively, we observe that seeudo-lbbeling phase adaptive threshold uses relatively uniform number of samples from each group to training the spurious attribute predictor.
5.3
SSA COMBINED WITH SUPERVISED CONTRASTIVE LEARNING
We now examine whether the proposed SSA still remains be beneficial when combined with other robust training procedures. As an example, we choose a recent robust training procedure (Zhang al., 2021) proposed an alternative to Group DRO. More specifically, CNC (Zhang et al., 2021) considers a following procedure base on the supervised contrastive loss (Khosla et al., 2020): As in JTT (Liu al., 2021), we first train standard ERM model. Then, we use the contrastive loss to maximize the representational similarity between samples have the same target label but different ERM prediction, while minimizing the representational similarity of samples with different target attribute but same ERM prediction. This procedure can be smoothly combined the SSA framework, by replacing the ERM predictions with the pseudo-labels generated in the first phase of SSA.
In Table 6, we compare the performance of SSA using Group DRO or CNC with the performance of the robust training methods using the full spurious attribute annotations on the training set. Both models learned with SSA achieved comparable performance to the fully supervised counterparts. This result suggests that the benefit of SSA may not be constrained on specific robust training method, and may be combined with more general classes of robust training algorithms. Also, find that none of the robust training method consistently outperform the other; CNC with group label slightly outperforms Group DRO Waterbirds, and Group DRO does better on CelebA.
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Table 7: Comparison of classification performance (bACC/GM) on CIFAR-10 under 4 different sets of mmijj Rmaj- Nab). where maajj denotes number of labeled samples in to the largest class, Reajj denotes the number of unlabeled samples in the largest class, and Nab denotes the hurgest-t--smalles class ratio in the labeled set (we let "Jnnlbb = 1). The best results are indicated in bold.
(Maz- Pmyj: Tlab)
Algorithm
4500, 100)
(500,4500, 50)
(100, 44000,0)) 50)
(100, 44000, 20)
Vanilla
42.1asm/ / 225
50.3ga1e / /41 41.622.0
28. / 13.0
34,421m/ / 26.4
FixMatch (Sohn et al., 2020)
65 63..5/335/ / 2
68. 3 30,4g021
56,4 / 14,9 -111
69.1 42, 12237
DARP (Kim al., 2020)
75.6=0a/ 78.2+214 73.1 +6.5
72.3+sas 67.8-
77.5+047 / 75.22am1
CReST (Wei et al., 2021)
77.4 61,6ga
79.7 / 17
67.41 34,7
69.8 / 53.6gnn
Adaptive thresholds (Ours)
86.1
85.7
87.4 /87.1=000
83.8sa10 82.7
86.5 / 16,1
Oracle
93.6+a.8 / 93.5
93.6+aus /93.5
93.6+a.1 / 93.6-
93.9+auz / 93.8+uud
5.4 APPLICATION TO GENERAL SEMI-SUPERVISED LEARNING UNDER CLASS IMBALANCE
In Section 4.2, we proposed to use adaptive thresholding mitigate confirmation bias when pseudo- labeling group-imbalanced datasets. Interestingly, it turns out that the benefit of this idea also ex- tends (without any modification) to a more general scenario of semi-supervised learning (SSL) on datasets with class imbalances. In fact, two settings are quite similar, except that SSA aims to put pseudo-labels on spurious attributes while SSL methods estimate target attributes. To demonstrate this point, we evaluate adaptive thresholds under the SSL setup, and compare it with the perfor- mances of baseline SSL method proposed without considerations on the class imbalance issue. DARP (Kim et al., 2020) and CReST (Wei et al., 2021) build on FixMatch to handle class imbalance, but are primarily de- signed under the assumptions that there exists sufficiently many labeled data at hand (to estimate the class imbalance ratio), and that class distributions of labeled and unlabeled datasets are identical, respectively." In contrast, our method of adaptive thresholds does not rely such assumptions to solve the same task.
labuudo-labling--ba based SSL algorithms. FixMatch (Sohn al., 2020) is a recent
We consider a slightly more challenging experimental setup than in Kim et al. (2020): We construct an artificial labeled dataset from the CIFAR-10 10 (Krizhevsky et al., 2009) by controlling the number of samples in the majority class mmjj (i.e., the largest class) and the ratio between the largest and smallest class sizes Mab 1.4 In similar manner, construct
unlabeled dataset using some parameters maaj and Tunlab- We select these parameters so that the number of labeled samples is small, and the imbalance ratios of labeled and unlabeled sets have bigger discrepancies (We provide further details in Appendix A.6). In Table 7, we observe that empirical gains from both DARP and CReST are limited comparing to supervised learning with full ground-trutt labels (oracle), due to the violation of their inherent assumptions in the experimental setups of our choice, i.e., limited labeled data and different class distribution between labeled and unlabeled datasets. However, our method successfully reduces such gap by effectively constructing the pseudo-labels for minority classes. Overall, these results implies that the proposed method has a potential to provide a more robust semi- semi-supervised learning solution (despite its simplicity) in more realistic scenarios, which we think is an interesting direction to explore further in the future.
6 CONCLUSION
In this work, we present Spread Spurious Attribute (SSA), an algorithm for improving worst-group accuracy in the presence of spurious correlation. SSA framework uses a small amount of spurious attribute annotated samples to estimate group identities of the training set samples. With the gener- ated attribute annotated training set, we successfully train robust model using existing worst-case loss minimization algorithms. SSA is highly effective given a limited amount of the spurious at- tribute annotated samples, but still does not completely remove the need for supervision on spurious attributes which is an important future direction.
'In fact, in the previous spurious attribute setups, adopting DARP/CReST methods did not provide much gain over naive pseudo-labeling we suspect the reason to he the violation of these assumptions. 'I arrer nab thus indicates more severe imbalance.

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ETHICS STATEMENT
This paper addresses the problem of mitigating the harmful effects of spurious correlations in the dataset, which is deeply intertwined with the topics of machine bias and fairness. By targeting de- mographic groups (e.g., gender, race) carefully, our method has a potential to be used for preventing the machine to make predictions on the basis of biases on such demographic identities. One possible pitfall, however, is that this group-based evaluation can provide a false sense of morality; as making a fair decision in terms of group cannot be equated with the individual faimess (even putting aside the issues gerrymandering), our performance reports based on the worst-group performance should not be naively taken measure of justice. About the responsible research practice: We have used benchmark datasets, and thus believe that our research practice has not posed any foreseeable hazard in this regard.
REPRODUCIBILITY STATEMENT
We provide descriptions of the experimental setup and implementation details in Appendices A.2, A.3 and A.5. Also, we provide our source code as a part of the open-to-public supplementary materials.
ACKNOWLEDGMENTS
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea govermmen((MSIT) (No.2019-0-0000) Artificial Intel- ligence Graduate School Program (KAIST) and No.2019-0-01396 Development framework for analyzing, detecting, mitigating of bias in AI model and training data)
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Algorithm Spread Spurious Attribute
1: Input: group-labeled set L. group-unlabed unlabeled set Du. number of training splits K, number of iterations T, confidence threshold "ynn" learning rate 7)
2: Split unlabeled dataset D Di)) DD
{Phase I: pseudo-labeling)
3: Split labeled dataset D,, Di + D
4: for k =.
do 5: Initialize the pseudo-attribute predictor (z; 0x)
6: Dj UK / Dj
186 for 1,.... ,
7:
8:
Draw a group-labeled mini-batch B,, { ,z(h) ((0)) from Dj
Draw group-unlabeled mini-batch By {(x10), ( from Di
9:
bb=1
10:
Update Ty for # Imin as minimum Tg satisfying Eq. (7)
11:
Update 0g + Voo (E(rya)EB, bup(2,, y. a) + Σ (x.))EB Lunsup (I, y)
12: end for
D
{ (1 fa(;; (z,v) D
14: end for
DU + -(
15:
16: Initialize the robust model f(x;0)
{Phase 2: robust training) Run Group DRO with group-label estimated training 0 GDR Du)
A EXPERIMENTAL DETAILS
A.1 SPREAD SPURIOUS ATTRIBUTE PSEUDOCODE
Algorithm provides pseudocode for Spread Spread Attribute combined with Group DRO.
A.2 DISCUSSIONS ON SPLITTING THE GROUP-LABELED AND GROUP-UNLABFLED SETS
Recall that in the pseudo-labeling phase of SSA (Section 4.1, we partition both the group-labeled set and the group-unlabeled set into subsets:
Dí DJ =u= UD;
(10)
SSA then trains a spurious attribute predictor based on Di, Di. with hyperparameters tuned using Di. The trained model is then used make predictions the samples in Dj.
Here, it is easy to see that, if we want to make a best prediction on particular data point x, E Dis then the optimal split would be the one that uses the largest number of group-unlabeled samples for training the model, i.c.,
Di Du (x.), Di = {x.).
(11)
However, such partitioning requires training [Dedl different models label all samples in the groupeudlabeled set, which is computationally infeasible. Thus, we propose partitioning the group- unlabeled dataset into K equally-sized subsets D(1) D(K) and run the algorithm K times, using
Di uK - s#f
D(),
Di = D()
(12)
for the i-th training iteration. For all experiments appearing in this paper, we used 3 for simple evaluation; the empirical performance of SSA may improve if we use larger value of K.
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Table 8:
Group-wise accuracy of spurious attribute predictor in pseudo-labeling phase CelebA. Results of the worst-performing group are marked in bold.
Amount of group label used
Non-blond
Blond
Method
Female
Male
Female
Male
SSA (Ours)
89.5
93.9
95.1
83.6
10% of val.
Wiithout group-umlabeled set split
86.9
91.5
94.9
76.3
SSA (Ours)
83.3
90.6
92.7
75.7
5% val. set
Without group-unlabeled set split
83.4
89.5
93.9
71.7
Table 9:
Group-wise accuracy of spurious attribute predictor in pseudo-labeling phase on Water- birds.
Results of the worst-performing group marked in bold.
Amount of group label used
Landbird
Waterbird
Method
Land
Water
Land
Water
SSA (Ours) Without group-unlabeled set split
92.1
96.2
94.6
93,2
10% of val. set
91.3
96.2
92.9
94.3
SSA (Ours)
85.1
94.6
91.1
91.6
5% of val. set
-Without group-unlabeled set split
85.2
94.6
91.1
92.5
For the group-labeled set, we do not require such trick. We simply partitioned the group-labeled into equally size subsets, and used one for training and another for validation. We kept the split fixed throughout the whole pseudo-labeling phase.
Discussion. The splitting procedure aims to mitigate the potential negative effect of "self- confirmation" that may take place in the pseudo-labeling procedure. During the training of pseudo- labeler, SSA utilizes the pscudo-attributes of the training samples whenever the prediction confi- dence exceeds certain threshold. In other words, when a sample gains a high-enough confidence, heeuo-labellng may continually strengthen its own predictions, and can be very problematic in group-DRO-likes scenarios where we expect severe group imbalance. Data splitting helps mitigate this effect by separating the samples that we train on and the samples we make final predictions on (as side note, we also propose group-adaptive thresholds to mitigate the same effect). Empirically, we indeed observe that data splitting helps improve the downstream worst-group performance. Ta- bles and 9 give comparison of SSA with/without splitting CelebA and Waterbirds dataset, respectively.
A.3 TRAINING DETAILS
Models. As briefly discussed in the main text, we use pretrained ResNet-50 and BERT for image and natural language dataset experiments. respectively. For ResNet-50, we use the torchvision implementation. For BERT. we use the huggingface implementation.
Hyperparameter Tuning Overall. We separately tune the hyperparameters for the pseudo- labeling phase and the robust training phase. For the pseudo-labeling phase, the tuning criterion is the worst-grous grou- spurious label classification accuracy on Di. For the robust training phase, the tuning criterion is the worst-group prediction accuracy the trained target attribute classifier on the whole validation set D,,, We fix the threshold Tem. for the group with smallest population 0.95 following (Sohn et al., 2020) in pseudo-labelinn phase.
Hy Hlyperparameter Tuning- Image. For Waterbirds and CelebA, we tuned the learning rate over {le- 3, le-4, 1c-5} and l2 regularization over [Ie-I, le-4]. We used SGD optimizer with momentum 0.9 and batch size 64. In seeudo-labeling phase, train the spurious attribute predictor 1k iterations for Waterbirds and 45k iterations for CelebA.
Hyperparameter Tuning Natural Language. For MultiNLI and CivilComments-Wiln not tune any hyperparameter and follow the details of Liu et al. (2021). ). For MultiNLI, we trained SSA and its baselines for 5 epochs with default tokenization and dropout, using the batch size 32 and
we did
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Table 10: Runtime of pseudo-labeling phase and robust training phase the datasets we considered.
Dataset
Waterbirds
CelebA
MultiNLI
CivilComments
Pyeudo-labeling phase
/ split
2 hrs / split
hrs split
hrs / split
Robust training phase (Group DRO)
2.3 hrs
12 hrs
6.2 hrs
12.5 hrs
the initial learning rate of 2e-5. We did not use any & regularization. For CivilComment--ILDDD. we capped the number of tokens per example at 300 and used batch size 8, & regularization of le-2 and initial learning rate of lc-5. We used AdamW optimizer with gradient clipping for both MultiNLI and CivilComments- -entmmentt-WWLDS. In pseudo-labeling phase, we train the spurious attribute predictor 30k iterations for both MultiNLI and CivilComments.
Hyperparameter Tuning Robust Training, For the robust model, we use hyperparameters to maximize worst-group accuracy Dval- Following Liu et al. (2021), we use learning rate of le- and l2 regurlarization le-1 for Waterbirds, learning rate of le-5 and &g regularization le-1 for CelebA. We SGD optimizer with momentum 0.9 and batch size 64 for both Waterbirds and CelebA. For MultiNI.I and CivilComments, we use the same configuration as pseudo-labeling phase, except using batch size 16 for CivilComments. In robust training phase, we train the robust model 300 epochs for Waterbirds and CelebA, 10 epochs for MultiNLI, and 5 epochs for CivilComments. We choose the best model based on the model selection criteria described above.
Baseline Implementation. For EIIL, we directly take results on Waterbirds and CivilComments from Creager et al. (2021), while the results on CelebA and MultiNLI are new. For environment inference on CelebA, we follow the same procedure as for Waterbirds. We use one epoch trained ERM as reference classifier. We optimize the objective with a learning rate of 0.01 for 20k steps using the Adam optimizer. For environment inference MultiNLI, follow the same procedure as for CivilComments- WILDS. We train an ERM model for 5 epochs and choose the reference classifier using the best epoch based the validation worst-group accuracy. We use the error split heuristic instead of optimizing EI objective as Creager et al. (2021) did. We then train the robust model with the same procedure as we did.
A.4 RUNTIME ANALYSIS
In Table 10, we provide the time required for the aseudo-labeling phase and the robust training phase on a single Nvidia Titan XP for each dataset. Compared with the vanilla Group DRO, SSA requires an additional pseudo-labeling phase. When we select K 3, the overhead is small as x0.5 on the CelebA dataset, and does not exceed x4 in the worst case (Waterbird). We make two additional remarks. First, other baseline methods for addressing the lack of full group annotation (c.g.. JTT, LiF) also require additional computation and runtime. For instance, JTT requires a preliminary training to estimate the spurious label. Second, the number of iterations used for pseudo- labeling has not been optimized for achieving the best runtime- matime-peerfomance tradeoff, and thus can be further improved with more careful search.
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A.5 DETAILS ON SUPERVISED CONTRASTIVE LEARNING
Given an anchor (1), original supervised contrastive loss uses same class samples (y = yi) as positive samples and different class samples (y * yt) negative samples maximize simi- larity of representation between samples belong to same class. Correct-N-Contrast (CNC; Zhang al. (2021)) first trains ERM model as JTT and use ERM prediction as surrogate to ground truth spurious attribute. With obtained ERM prediction, CNC uses supervised contrastive loss to maximize similarity of representation between samples having same target and different ERM pre- diction, while minimizing similarity of representation between samples having different target and same ERM prediction. Similar to the second stage CNC, given an anchor (Iz, Vi, a;), we use sam- ples having same target and different spurious attribute as positive samples (v* Di, at + a,) and samples having different target and same spurious attribute as negative samples (y * Ys. a = az). To be specific, we sample M samples from each group. Given anchor (24, Vr, a,). we use sam- ples from group (yi, a at) for at * a, as a set of positive samples B and samples from group (yT,a) for y * yi set of negative samples B In addition standard cross entropy loss, minimizes following supervised contrastive loss for each anchor Ny²
explz; x+ /T)
Σ stEB
log
(13)
B
Σ.EBtUB exp(2: 2/T)²
where: 2 = g(x) is a representation of x and g X + Rd an encoder maps r to a representation, == h 9. > 0 is a scalar temperature hyperparameter.
We use M = 16 for both Waterbirds and CelebA. Except batch size, we follow Zhang et al. (2021) for other hyperparameters. For Waterbirds, optimizer with momentum 0.9, learning rate le-4, weight decay le-3 and use gradient accumulation to update model parameter every 32 batches. For CelebA, use temperature 0.05, contrastive weight 0.75, SGD optimizer with momentum 0.9, learning rate le-5, weight decay 1e-1 and use gradient accumulation to update model parameter every 32 batches.
temperature 0.1, contrastive weight 0.75, SGD
A.6 DETAILS ON CLASS- IMBALANCED SUPERVIEE LEARNING
We consider a classification problem with K classes. In other words, our goal is to train predictor X - y with y = (l,..., K). We assume that we have access to two types of datasets: labeled, and unlabeled. We let
Dr = {(11, .i)...,, (fEm }m)), Du = fai....,.... Fnd
(14)
The number of samples in some class l E y will be denoted by my and ng. respectively, i.c., Ek= My = n and Σ my = m. Without loss of generality, we assume that the number of labeled data in each class is ordered descending order, i.e.,
Mmajj =: 2 m2
mK-
(15)
We define the imbalance ratio of this labeled dataset as the ratio between the sample sizes of the largest class and the smallest class, i.e., Nab = m1/mk Z 1. This quantity used as a key parameter that controls the degree of class imbalance in the labeled dataset; higher the Nab. more severe the imbalance. To determine the number of samples in other classes, we use an exponential decay function, i.e., my =
(-R-1)/(K-1) We assume that the same ordering holds for the unlabeled samples, i.e., 71 2 ... 2 nKK and let define the imbalance ratio as "fnllab = / For simplicity, we let Junlab = 1, i.e., all classes have the same number of unlabeled samples.
Mab
To evaluate the classification performance of models trained under the imbalanced dataset, we re- port two popular metrics: balanced accuracy (bACC) and geometric mean scores (GM), which are defined by the arithmetic and geometric mean over class-wise sensitivity, respectively. Mean and standard deviation are reported across three random trials, respectively.
16


Reviews:
[{"review_id": "svi4Rpw3d7i", "reviewer": "Reviewer_dgzg", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "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": "Strengths:\n- The empirical results on worst-group accuracy are strong, even with a small number of attribute-annotated examples.\n- The proposed semi-supervised approach seems promising, both for the spurious attribute setting and even the general class-imbalanced SSL setting, as shown in Section 5.4.\n\nWeaknesses:\n- The language of training and validation is confusing and imprecise, as the \"validation\" samples are actually used for training as well. The authors should instead clarify that some training examples have attribute annotations and some do not, and similarly for the validation set.\n- The methods description is hard to understand in some parts. Specifically, why are predictions not made in D_{train}^{circ} (as mentioned at the end of Section 4.1)? In principle, the model could make predictions for these, but no clear reason is given why this is not done.\n- Related to the previous point, one would expect the proposed algorithm to be very computationally intensive. First, the semi-supervised learning stage will likely be expensive. Next, since this is repeated K=3 times, that makes it even more expensive. (And finally, the Group DRO model still needs to be trained after that.) Computational cost / runtime results are not provided.\n- The paper is unclear about some hyperparameter details such as the number of epochs for semi-supervised learning.\n- It would be great to better understand why the proposed approach performs so well. Based on Table 3 there is almost no drop in performance until we reach 5% of the original val set size, and even then the drop is small. In this case, on Waterbirds, we would only have 3 annotated examples for worst group for training the spurious attribute predictor (and perhaps fewer if any of these are actually used for validation, rather than training itself?). What is the accuracy of the spurious attribute predictor on the worst group - if it is high, how and why might this be the case (despite the extremely low number of annotations)? If it is low, why does the robust model still attain good worst-group performance?\n\nQuestions:\nIn Table 6, are the SupCon results actually vanilla SupCon or the procedure from Zhang et al. (2021)? Based on the writing, it seems the latter, but the table suggests the former.", "summary_of_the_paper": "This paper studies the group robustness problem in the setting of spurious correlations, when only a small number of samples have spurious attribute annotations. They present a pseudolabeling algorithm based on FixMatch to pseudolabel the remaining examples, and then use worst-case loss minimization algorithms such as Group DRO to train a more robust model.", "summary_of_the_review": "Overall, this paper has strong empirical results. The paper could be improved with more precise / clear descriptions of the methods and design decisions, and additional ablation experiments or metrics that help explain how the methods actually perform so well even in the extremely low-annotation setting (in lieu of theoretical analysis, which the paper does not have). Thus, my current score is a weak acceptance.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and 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": "nFW32Nenan0", "reviewer": "Reviewer_a6uc", "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": "Strengths：\n\n1. This paper is well-written and well-motivated. The proposed spread spurious attribute method is novel. \n\n2. Comprehensive experimental results demonstrate the effectiveness of the proposed approach. \n\n3. The proposed approach is a general yet effective framework that can be applied to lots of tasks.\n\n ", "summary_of_the_paper": "This paper proposes a pseudo-attribute-based algorithm, coined Spread Spurious Attribute (SSA), for improving the worst-group accuracy. The proposed method leverages samples both with and without spurious attribute annotations to train a model to predict the spurious attribute, then uses the pseudo-attribute predicted by the trained model as supervision on the spurious attribute to train a new robust model having minimal worst-group loss. Experimental results on various benchmark datasets show that the algorithm consistently outperforms the baseline methods using the same number of validation samples with spurious attribute annotations. ", "summary_of_the_review": "Overall, this paper proposes a novel and general yet effective framework. The paper is well-motivated and well-written. Moreover, comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed approach.", "strength_and_weaknesses": "", "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": "jbs3cmIXmMG", "reviewer": "Reviewer_qZ2x", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "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": "## Long summary\nPrevious methods try to identify and upweight samples from minority groups (spurious attribute joint with class label). Then a validation set of annotated samples are used to tune hyperparameters. The performance of such models are very sensitive to these hyperparameters/validation set, and therefore, fail to perform comparably with methods that use the spurious attribute. This work aims to resolve this issue to achieve performance similar to methods with annotations.\n\nSSA first does pseudo labeling and then does robust training. It trains a domain index predictor and uses a threshold to solve the confirmation bias issue by balancing each group. Then pseudo labels are used on the training set. Then group DRO is used for robust training.\n\nThe confidence of the majority group increases faster than the minority group, which is even more severe when the label is pseudo label. This is so-called confirmation bias.\n\nIn experiments, it uses the datasets widely used in this field: Waterbirds, CelebA, MultiNLI and CivilComments-WILDS. They show SSA improve the worst group accuracy significantly over existing methods without spurious attribute even with much less valiation data. They also analyze the influence of the validation set size. Finally, they analyze the pseudo labeling process of SSA to verify the adaptive thresholding is useful. They also show SSA works with supervised contrastive learning loss and can be useful in semi supervised learning with imbalanced classes.\n\n\n## Strength:\n\n1. Very comprehensive experiments.\n2. Relatively simple and reproducible method.\n\n## Weakness:\n\n1. Lack of very strong theoretical justification. It is hard to have a straightforward interpretation of why this works, especially based on pseudo labels.\n2. Some experiments seem not relevant to the problem this paper aims to solve.\n\n## details and questions\n1. Table 6 is kind of redundant since most of it overlaps with Table 1.\n2. It is not clear why the supervised contrastive learning part is needed in this work. The same to the semi-supervised learning experiments.\n3. How are the supervised and unsupervised loss combined (eq.3)?\n4. Why do we need to split the training data in 4.1? They don't have spurious attributes anyway and the method only infers them on training data.\n5. I believe it can also be compared with EIIL [1].\n\n[1] Creager, Elliot, Jörn-Henrik Jacobsen, and Richard Zemel. \"Environment inference for invariant learning.\" International Conference on Machine Learning. PMLR, 2021.", "summary_of_the_paper": "This work focuses on the worst group optimization (distributional robust optimization) problem with few spurious attributes are available as a validation set. They aim to achieve similar performance with methods that use spurious attributes. The main contribution is an adaptive thresholding method to ensure balanced sample from each group. Experiments show the proposed pseudo labeling and thresholding are effective in various tasks.\n", "summary_of_the_review": "## Strength:\n\n1. Very comprehensive experiments.\n2. Relatively simple and reproducible method.\n\n## Weakness:\n\n1. Lack of very strong theoretical justification. It is hard to have a straightforward interpretation of why this works, especially based on pseudo labels.\n2. Some experiments seem not relevant to the problem this paper aims to solve.", "strength_and_weaknesses": "", "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": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "jKva2H9L2z", "reviewer": "Reviewer_kQbw", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "This paper tackles an important problem and provides a convincing demonstration that current SOTA models are susceptible to reliance on spurious correlation in the training set. I detail key strengths, weaknesses, and additional questions below.\n\n### Strengths\n- Reducing the amount of annotations required worst-group loss minimization is an important problem, and this paper presents a simple procedure to help with that problem. \n- The empirical portion of the paper seems quite thorough, and the work compares against several recent work in this line.\n- This paper includes ablations of different components of the proposed formulation to help understand what each one contributes.\n- It seems like the SSA approach might be useful when applied with robust training approaches in general.\n\n### Weaknesses, Concerns, & Additional Questions\n- My biggest issue is that the paper has several moving parts in Sec. 4.1 and 4.2, which are really the key contributions of this work. I am not sure how to improve the writing here, but there are several pieces like the different validation and training sets, the two different losses, and then the different group thresholds. Even though it seems clear, it was challenging to be sure that all the pieces fit together.\n- I am surprised that the approach works so well given the data size disparity between D_train and D_val. I am not that familiar with the performance of standard unsupervised methods. Is such size disparity usually the case?\n- In section 4.2, what does \"pseudo-group population ratio of the highly-confident samples\" mean?\n- It seems like the whole point of the group specific thresholding is to help make sure that the model for learning the spurious/group attributes perform well on the small sample groups. First, I don't think I would call this 'confirmation bias' as this paper does, unless this is a standard term in the unsupervised learning literature. Second, it is unclear to me why the approach used for setting this threshold should be effective. The minimum threshold is initially set on the basis of the size of the smallest group, however, consider the unlikely case where that small group is just n copies of the same sample or somehow homogeneous and low variance. This small group would be more easy to learn than a larger group with larger 'variance' or more 'diversity'. Essentially, I am hoping the authors can clarify why this scheme should be effective on the basis of intuition or a toy example. To be more specific, equation 6 seems critical here. I don't understand why this requirement was chosen, and I worry that the dependence on size of the groups is somewhat unusual.\n- To clarify, pseudo-group is $\\hat{a}(x)$ for the points in the training set?\n- There is an important yet simple experiment that I think is missing. Why can't the authors check the accuracy of their SSA scheme for a dataset where we have the ground truth spurious attributes like the waterbirds and the other datasets that they use? Is this what Table 4 is showing? If yes, then it might help to clarify the caption and add more clarification.\n\nPost Rebuttal\nSatisfied with the author response, so recommending that the accept.", "summary_of_the_paper": "This paper presents a technique, spread spurious attribute, for inferring the group annotation for training samples in a dataset. The inferred group information is then used as part of a group DRO minimization scheme or some other worst case scheme. The key insight in this work is to use a small validation set (1-5 percent) of data that has annotations to learn annotations for the entire training set via pseudo-labelling. The labelling scheme presented uses different thresholds for the different spurious groups. This new dataset is then used as part of a new DRO pipeline.  ", "summary_of_the_review": "Overall, this paper conducts thorough empirical analysis of the SSA approach, which can be incorporated with robust training methods. I recommend a weak accept because there are portions of the scheme that I think needs better justification. ", "strength_and_weaknesses": "", "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": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a new method to decrease the supervision cost for learning spurious attributes using worst-group loss minimization. Their method uses samples both with and without spurious attribute annotations to train a model to predict the spurious attribute, then use the pseudo-attribute predicted by the trained model as supervision on the spurious attribute to train a new robust model having minimal worst-group loss. The experiments show promising results in this domain for reducing annotation cost. 

The reviewers vote to accept the paper, and some of them increased their scores during the discussions since the authors have addressed their concerns.
Decision: Accept (Poster)