MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Authors: Saeid Asgari
Aliasghar Khani
Fereshte Khani
Ali Gholami
Linh Tran
Ali Mahdavi-Amiri
Ghassan Hamarneh
Venue: NeurIPS 2022 Accept (2022)
Keywords: spurious correlations
selective classification
shortcut learning

Abstract: A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features. This work proposes MaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTune forces the trained model to explore new features during a single epoch finetuning by masking previously discovered features. MaskTune, unlike earlier approaches for mitigating shortcut learning, does not require any supervision, such as annotating spurious features or labels for subgroup samples in a dataset. Our empirical results on biased MNIST, CelebA, Waterbirds, and ImagenNet-9L datasets show that MaskTune is effective on tasks that often suffer from the existence of spurious correlations. Finally, we show that \method{} outperforms or achieves similar performance to the competing methods when applied to the selective classification (classification with rejection option) task. Code for MaskTune is available at https://github.com/aliasgharkhani/Masktune.

MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Saeid Asgari Taghanaki* Autodesk Al Lab
Aliasghar Khani* Autodesk Al Lab
Fereshte Khani* Stanford University
Ali Gholami Autodesk AI Lab
Linh Tran Autodesk AI Lab
Ali Mahdavi-Amiri Simon Fraser University
Ghassan Hamarneh Simon Fraser University
Abstract
A fundamental challenge of over pee-paaraeterizeed deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features. This work proposes MaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTinee forces the trained model to explore new features during single epoch finetuning by masking previously discovered features. MaskTune, unlike earlier approaches for mitigating shortcut learning, does not require any supervision, such as annotating spurious features or labels for subgroup samples in a dataset. Our empirical results on biased MNIST, CelebA, Waterbirds, and ImagenNet-99. datasets show that MaskTune is effective tasks that often suffer from the existence of spurious correlations. Finally, we show that MaskTune outperforms achieves similar performance to the competing methods when applied to the selective classification (classification with rejection option) task. Code for MaskTume is available https: //te:///ttttttt com/ al allaahharkhanii Masktune.
Introduction
Spurious correlations are coincidental feature associations formed between a subset of the input and target variables, which may caused by factors such as data selection bias [TTorallaa and Efros, 2011, 1, Jabri et al., 2016]. The presence of spurious correlations in training data can cause deep neural networks to fail, often drastically, when such correlations do not hold test data [Sagawa et al., 2019] or when encountering domain shift [Arjovsky et al., 2019]. Consider the classification problem of cows and camels [Beery et al., 2018], where most of the images of cows vs. camels captured on green fields vs. desert backgrounds due to selection bias (and perhaps the nature of the problem that camels are often in the desert). A model trained on such data may rely on the background as the key discriminative feature between cows and camels, thus failing on images of cows on non non-green backgrounds or camels on non-desert backgrounds.
parameterizee
In prrameermettee regimes, there are often several solutions with almost identical loss values, and the optimizer (e.g., SGD) typically selects a low-capacity one [Wilson al., 2017, Valle-Perez et al., 2018, Arpit et al., 2017, Kalimeris et al., 2019]. In the presence of spurious correlations, the optimizer might choose to leverage them as they generally demand less capacity than the expected semantic cues of interest, e.g., relying on the local color texture of grass instead of the elaborate visual features that give a cow its appearance [Bruna and Mallat, 2013, Bruna et al., 2015, Brendel and Bethge, 2019, Khani and Liang, 2021].
These authors contributed equally this work.
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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Raw input
Masked Input
with spur
Mask
Single epoch finetune
ERM
ERM
without spuncus features
Class activations
MaskTune
The discriinnatvve
Mtealod Sewtures
disoriminatvve MaskTune
Figure I: MaskTune generates a new set of masked samples by obstructing the features discovered by a model fully trained via empirical risk minimization (ERM). The ERM model is then fine-tuned for only one epoch using the masked version of the original training data to force new feature exploration. The features highlighted yellow, red, and green correspond to features discovered by ERM, the masked features, and the newly discovered features by MaskTune, respectively.
In previous work, supervised loss function has been employed to reduce the effect of spurious correlations [Sagawa et al., 2019]. However, identifying and annotating the spurious correlations in large dataset as a training signal is impractical. Other works have attempted to force models to discard context and background (as spurious features) through input morphing using perceptual similarities [TTaganakii et al., 2021] or learning casual variables [Javed et al., 2020]. Discarding context and background, however, is incompatible with the human visual system that relies on contextual information when detecting and recognizing objects [Palmer, 1975, Biederman al., 1982, Chun and Jiang, 1998, Henderson and Hollingworth, 1999, Torralba, 2003]. In addition, spurious features may appear on the object itself (e.g., facial attributes). Thus discarding the context and background may be futile strategy in these cases.
Instead of requiring contextual and background information to be discarded or relying on limited number of features, we propose a single-epoch finetuning technique called MaskTime that prevents model from learning only the "first" simplest mapping (potentially spurious correlations) from the input to the corresponding target variable. MaskTune forces the model to explore other input variables by concealing (masking) the ones that have already been deemed discriminatory. As we finetune the model with new set of masked samples, we force the training to escape its myopic and greedy feature-seeking approach and encourage exploring and leveraging more input variables. words, as the previous clues are hidden, the model is constrained to find alternative loss-minimizing input-target mappings. MaskTune conceals the first clues discovered by fully trained model, whether complementary
other
they are spurious not. This forces the model investigate and leverage discriminatory input features. A model relying upon a broader array of complementary features (some may be spurious while others are not) expected to be more robust to test data missing subset of these features.
Figure visualizes how MaskTune works via a schematic of the cow-on-grand on--oas-sgss scenario. Even in the absence of spurious correlations, models tend to focus the shortcut (e.g., ears skin texture of cow), which can prevent models from generalizing to scenarios where those specific parts are missing. However, the object is still recognizable from the remaining parts. As an alternative, MaskTune generates diverse set of partially masked training examples, forcing the model to investigate a wider area of the input features landscape c.g., new pixels.
A further disadvantage of relying on limited number of features is the model's inability to know when it does know. Let's go back to the cow-camel classification example; if cows only appear on grass in the training set then it is unclear which of the grass or the cow refers to the "cow" label. model that only relies on the grass feature can confidently make a wrong prediction when some other object appear in the grass in the test time. We need the model to predict only if both cow and grass appear in the picture and abstain otherwise. One method used in the literature to address this issue is selective classification [Geifman and El- El-Yaniv, 2019, 2017, Khani al., 2016), which prediction. Selective classification
allows network to reject sample it is not confident is essential in mission-critica. applications such autonomous driving, medical diagnostics, and

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robotics as they need to defer the prediction to human if they are uncertain about the prediction. Learning different sets of discriminatory features, in addition to reducing the effect of spurious features, enables MaskTune to be applied to the problem of selective classification.
We apply MaskTine to two main tasks: robustness to spurious correlations, and b) selective classification. We cover four different datasets under (a) including MNIST with synthetic spurious features, CelebA and Waterbirds with spurious features in different subgroups [Sagawa et al., 2019], and the Background Challenge [Xiao et al., 2020] which is dataset for measuring the reliance of methods on background information for prediction. Under (b) we test MaskTime on CIFAR- 10 [Krizhevsky et al., 2009], SVHN [Netzer et al., 201 II, and Cats vs. Dogs [Geifman and El- Yaniv, 2019] datasets. On both tasks, we outperform or perform similarly the previous complex methods using our simple technique.
To the best of our knowledge, this is the first work to present finetuning technique using masked data to overcome spurious correlations. Our contributions are summarized as follows:
We propose MaskTune, a new technique to reduce the effect of spurious correlations or over-reliance limited number of input features without any supervision such object location or data subgroup labels.
2. We show that MaskTune leads to learning model that does not rely solely on the initially discovered features.
3. We show how our method can be applied to selective classification tasks.
4. We empirically verify the robustness of the learned representations spurious correlations on variety of datasets.
2 Method
Setup. We consider the supervised learning setting with inputs x E X' C Rdd and corresponding labels E y (1,...., k).
..]....................... We assume having access to samples DO = { 11) }= drawn from an unknown underlying distribution Pdato (I. y)-
Our goal is to learn the parameters 0 E θ of a prediction model ma X + y that obtains low classification error w.r.t some loss function (e.g., cross entropy) é: O (X' y) IR. Specifically, we minimize:
(() = (e (l(mm(x)) v)] R
((mo(z:).
(1)
where n is the number of pairs in the training data.
Besides having good prediction accuracy, we aim to develop a model which does not solely rely on spurious or a limited number of input features. We propose to mask the imput training data create new set. We then finetune (for only one epoch) a fully trained ERM model with the new masked data to reduce over-reliance on spurious or a limited number of features. The single epoch fine-tuning is done using a small learning rate e.g., the last decayed learning rate that the ERM has used in the first step. We found that large learning rates or more than one epoch fine-tuning leads forgetting the discriminative features learned by the ERM.
Input Masking. A key ingredient of our approach is a masking function that is applied offline (i.e., after full training). The goal here is to construct a new masked dataset by concealing the most discriminative features in the input discovered by model after full training. This should encourage the model to investigate new features with the masked training set during finetuning. As for G, we adopt the xGradCAM [Selvaraju et al., 2017], which was originally designed for a visual explanation of deep models by creating rough localization maps based on the gradient of the model loss w.r.t. the output of desired model layer. Given an input image of size H W x C. xGradCAM outputs a localization map A of size H x W X 1, which shows the contribution of each pixel of the input image predicting the most probable class, i.e., it calculates the loss by choosing the class with highest logit value (not the true label) as the target class. After acquiring the localization map, for each sample (æ,, yi), where I; E X and y/ E Y, we mask the locations with the most contribution as:
3

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i - - 0 xs:
Ar; = (mmo(x)) 26)
(2)
where T refers to a thresholding function by the threshold factor r (i.e., T = 1 <r): and @ denotes element-wise multiplication. As the resolution of A is typically coarser than that of the input data, T(A,,) is up-sampled to the size of the input.
Procedurally, we first learn model mitutialll using original unmasked training data Dinitial, Then we use migitial 9 and T to create the masked set Dinaked Finally, the fully trained predictor migitial is tuned using Dmmaseed to obtain m3 final
As for the masking step, any explainability approach can be applied (note that some may have more computational complexity, such as ScoreCAM [Wang et al., 2020]). We use XGradCAM [Selvaraju al., 2017] as it is fast and produces relatively denser heat-maps than other methods [Srinivas and Fleuret, 2019, Selvaraju et al., 2017, Wang et al., 2020].
2.1 Tune in Over-parameterized Regimes
Consider the overparametrized regime, in which the model family has sufficient complexity to fully fit the training data. It has been shown that deep neural nets are overparametrized and can fit completely random data [Zhang et al., 2021]. The generalization ability of deep neural nets is still not clear, but there are some speculation that connect the deep network generalization to their tendency of choosing simple functions that fit the training data [Valle-Perez et al., 2018, Arpit et al., 2017]. However, this simplicity bias can cause side effects such as their poor performance with respect to adversarial examples [Raghunathan et al., 2019] or to distribution shifts [Khani and Liang, 2021, Shah et al., 2020]. Here we study the effect of masking input features complexity of a model in situation where indeed the training procedure chooses the least complex model that fits training data. We show that in this case masking will result in learning more complex model that discovers features as the previous ones are blocked.
Formally, let C denote a function that measures model complexity and assume that the masking function T (as described 2) only returns binary values, i.e., an indicator function that only keeps some features and zeros out the rest. We show that if training procedure returns the least complex model then masking results in a more complex model.
Proposition Consider an optimizing procedure that finds min C(mo), s.t., f(mo) = as defined in 1. Let masking function T return binary values. If both models mg and mg (i.e., zero loss) then we have C C(mig (miii 2 C(m (piiiitaa))
initiol fit the training data
Proof. Note that both models belong the model family (miiitial mm .mfiaall O), and they both fit the initial
training data. In the first step, training procedure chooses migitiall over my miniaa,, therefore according to our assumption C(m 2 (miigiia)).
2.2 Adapting Mune for Selective Classification
Here we show how to use MaskTume for the selective classification problem. In order to make a more reliable prediction, we ensemble the original model (ml (migitial) and MaskTune (mffal) and only predict if both models agree. As a result, if there exist two sets of features that can predict the label, our method only predicts if both agree on the label (e.g., grass and cow in Figure 1).
To get an intuition on the performance of MaskTune for selective classification, similar to [Khani et al., 2016] we analyze the noiseless overparametrized linear regression. We show that MaskTune adaptation, explained above, abstains in the presence of covariate shift, thus leading to a more reliable prediction.
In particular, we show that MaskTune only predicts if the relationship between masked and unmasked features in training data holds in the test time. For example, if features describing "cow" are predictable of "grass" in training data (i.e., they always co-occur) then we only predict they co- occur in the test data as well. Formally, let denote the masked feature after the first round and denote the rest of the - 1 features. As we are in the overparametrizee regime, can be predicted from 2, let 3 be the min-norm solution for predicting from 2, i.c., B for training data. We

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now show that both models agree with each other for a new test set if s STz z in the test time as well. In other words, there is not covariate shift between and 2.
Let S E Rd be the concatenation, across all training samples, of single masked scalar feature, out of d possible features, and % E [Ra((d-1) be the remaining d- features. Let the model family θ be the linear functions, and the optimization solution that fits the training data. The models trained with and without S agree on the predicted output for a new test set (z, 8), iff = (Z) (ZZ")-
Proposition 2.
chooses the min Lg-norm
The full proof is in the appendix. Note that (Z (ZZ")- is the min-norm solution for predicting from in the training data. This proposition states that the models agree only if the relation between the masked feature (s) and remaining features (2) in training holds in the test data as well.
In practice, we need to trade off between the coverage and precision. Therefore, instead of a hard threshold and predicting only if both models agree, ensemble mig mnjiitial and m migiaal by multiplying their probabilities. In order to achieve the coverage goal, we find the desired threshold for abstaining in the validation set (see Section 4 for details).
3 Implementation Details
Classification with Spurious Features. For all of the datasets in Section 4.1, we use SGD with momentum of 0.9 and weight decay of le-4, For biased MNIST, we used a simple convolutional neural network with four convolutional layers and a linear head. We trained this model with a batch size of 128 for 100 epochs with a learning rate of 0.01. We decreased the learning rate by a factor of 0.5 every 25 epochs. In the case of Background Challenge, we trained an ImageNet pre-trained ResNet50 with a batch size of 1,024 for 100 epochs decaying the learning rate by 0.1 after every 30 epochs. For the CelebA dataset, trained ImageNet pre-trained ResNet50 with batch size of 512 for 20 epochs with a learning rate of le-4.
Selective Classification. For all the datasets in Section 4.2, we trained ResNet32 from scratch. For CIFAR-10, we used SGD with a momentum 0.9, weight decay of le-4, learning rate of 0.1, and batch size of 128. We trained it for 300 epochs and halved the learning rate every 25 epochs. For SVHN, we used the same hyperparameters as CIFAR-10 10 with only two differences: the weight decay and batch size were 5e-4 and 1,024, respectively. For Cats vs. Dogs dataset, we used Adam optimizer with a weight decay of le-4 and learning rate of 0.001. We trained the model for 200 epochs with a batch size of 128 and dropped the learning rate by factor of 0.1 on epoch 50.
Masking Threshold. Our goal is to mask the most important features, i.e., the core of the heat maps generated by the explainability methods. Masking a few input variables has almost no effect on the model's 's behavior, whereas large masks may destroy useful signals in the input, resulting in very low training accuracy. To reduce the search space of the masking threshold across all tasks, we experimented with т = {ps, 14 + o., μ4; + 20, p4 + 30,} where μ1; and o; represent mean and standard deviation over the heatmap values for training sample Tj. We also experimented with mean value over all training samples, using soft masks (i.e., no threshold), and sorting and masking the K-top activated variables. We found μ4 + 20, works better in general as μ4 and μ4 o, remove large portion of the input while b4 + 30, removes very few variables.
4 Experimental Results
We evaluate our MaskTune method on two main applications: a) classification with spurious comelations-we expect MaskTune to prevent such correlations by identifying and masking them and b) selective classification where the ability to abstain critical expect MaskTune to improve reliability by forcing model to investigate additional variables in the input and learn more complex relationships. In each experiment, we compare our method to relevant baselines and competing methods.
4.1 Classification with Spurious Features
MNIST with Colored Squares on the Corners: As a warm-up, we test the ability of our method to distinguish between two MNIST digit groups (0-4 and 5-9) in the presence of spurious features.
5

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18.46 26,25
original Slesed
Input
ERM
MuskTune
0
D
D
classification accuracy
GD 7B
60
3
3
3
40
20
2
2
MaskTune RandMask
ERM
Figure 2: Left: Test accuracy on the original and biased MNIST datasets. MaskTume outperforms other methods on both the original and biased test sets. Right: xGradCAM visualizations before (ERM) and after applying MaskTune. MaskTume enforces exploring more input features, which leads to more robust predictions. Here the spurious feature the blue square on the top left corner.
We construct dataset such that classifier would achieve poor accuracy by relying spurious input features. To this end, we group digits labeled to 4 into class 0 and those labeled 5 to 9 into class 1. Next, in the training set, place 99% and 1% of the new class O and new class I data background with a small blue square on the top left corner, respectively, and keep the remaining data intact. We use two test sets during testing: the original raw MNIST test set and biased test set. To create the biased test set we place all of the digits from the group "5-9" on a background with a small blue square (representing spurious features) on the top left corner and keep all of the digits from the group "0-4" unchanged (i.e., we don't 't add any squares to their background and use the original images). Figure 2 demonstrates the performance of the ERM, RandMask, and our MaskTune on both original and biased test sets. The RandMask method is similar to MaskTune, but masks randomly chosen area of the input image. As shown in Figure 2, MaskTune outperforms other methods by a large margin on both test sets. As demonstrated in Figure 2 (right), MaskTune forces the model to explore more features,
opposed to the ERM, which only looks into the spurious feature (the blue square). We also ran an experiment with multiple spurious features (Appendix ??) and reported results for iterative version of MaskTune.
Classification with Spurious Features in Subgroups. In this experiment, we leverage the CelebA [Liu et al., 2015] and the Waterbirds [Sagawa et al., 2019] datasets. In the CelebA dataset, there is high correlation between features gender=(male, female] and hair sair_color=(blond dark), meaning that the feature gender might be used as a proxy to predict the hair_colon In other words, an ERM would assign the label dark to male images since the majority of male images have dark hair, and there are only 1,387 (0.85%) blond males in the training set of size 162,770. To balance the accuracy of predictions on different subgroups of data, the existing methods [Sagawa et al., 2019] use subgroup information, i.e., wrong predictions of an ERM on the worst group (blond males) can be penalized during training. Some other approaches [Levy et al., 2020, Nam et al., 2020, Pezeshki al., 2021, Taghanaki et al., 2021, Liu et al., 2021] use subgroup information during model selection (labeled validation set). However, it is impractical to recognize "all" subgroups in dataset and label them. In Table 1, we show that MaskTinee achieves comparable performance both these groups of methods without using group information in training or in model selection. We also show MaskTune significantly improves worst-group accuracy (78% vs. 55% classification accuracy) in comparison to methods that do not use subgroup information during training or model selection. In Figure 3 (left), we highlight the important imput features for predicting hair color on the CelebA dataste. As shown, the ERM model leverages gender features while MaskTune forces investigate other features as well.
The Waterbirds dataset [Sagawa et al., 2019] was proposed to assess the degree to which models pick up spurious correlations the training set. We discovered and fixed two issues with the Waterbirds dataset: a) because the background images in the Places dataset [Zhou et al., 2017] may already contain bird images, multiple birds may appear in an image after overlaying the segmented bird images from the Caltech-UCSD Birds-200-201 1 (CUB) dataset Wah et al., 2011]. For example, the label of an image may be "landbird"", but the image contains both land and water birds. We manually removed such images from the dataset. b) Because the names of the species are similar, some land

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Table 1: Results from the CelebA dataset using ResNet-50. Our method outperforms all fully unsupervised methods.
Group labels in training set
Group labels
Worst-group accuracy
Avgerage accuracy
Method
in validation set
Group DRO Sagawa al. [2019]
Yes
Yes
88.3
91.8
CVaR DRO Levy et al. [2020]
No
Yes
64.4
82.5
LfF Nam et al. [2020]
No
Yes
77.2
85.1
SD Pezeshki al. [2021]
No
Yes
83.2=#.00
91.6:10.6
JTT Liu et al. [2021]
No
Yes
81.1
88.0
CIM Taghanaki al. [2021]
No
Yes
81.3
89.2
ERM Sagawa et al. [2019]
No
No
47.2
95.6
CVaR DRO Levy et al. [2020]
No
No
36.1
82.5
LfF Nam et al. [2020]
No
No
24.4
85.1
JTT Liu et al. [2021]
No
No
40.6
88.0
DivDis Lee et al. [2022]
No
No
55.0
90.8
MaskTune (ours)
No
No
78.0
91.3100.1
Input
ERM
MaskTune
Input
ERM
MaskTune
Figure 3: Activation visualizations of ERM and MaskTune for CelebA (left) and Waterbirds (right) samples. MaskTime enforces exploring new features. As demonstrated, after applying MaskTinee, the task-relevant input signals (hair colour and bird features) are emphasised.
birds have been mislabeled waterbirds which corrected. The corrected Waterbirds dataset can be found on MaskTune's 's GitHub page. After addressing the two issues, the ERM model's worst-group accuracy increased from 60% which is reported in [Sagawa et al., 2019] to 80.8+1.3% We repeated the group-DRO method and ERM experiments on the corrected waterbirds dataset and reported the results in Table 2. As demonstrated, our method (without any group supervision) achieves similar accuracy to the group- group-DRO DRO that benefits from full supervision. In Figure (right), we visualized feature importance before and after applying our MaskTune the modified Waterbirds dataset.
The Background Challenge.
further step, we evaluated MaskTune on the Background Challenge data [Xiao et al., 2020] to see if the positive observations from the MNIST experiment apply to

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Table Results from the Waterbirds dataset using ResNet-50. Our method significantly improves ERM's worst- group accuracy without supervision. Group labels in training set
Group labels
Worst-group accuracy
Avgerage accuracy
Method
in validation set
GroupDRO [Sagawa et al., 2019]
Yes
Yes
89.3+:3.1
94.4++0.7
ERM
No
No
80.8= 1.3
94.0=+0.2
MaskTune
No
No
86.4=1199
93.0=+0.7
more realistic scenario. The Background Challenge is a publicly available dataset that consists of ImageNet-9 [Deng et al., 2009] test sets with various levels of foreground and background signals. intended to assess how much deep classifiers rely spurious features for image classification. We used two configurations to compare MaskTune's performance: Only FG, in which the background is completely removed, Mixed-same, which the foreground is placed on different background from the same class, and Mixed-rand, where the foreground is overlaid onto a random background.
As shown in Table 3, MaskTune outperforms the baseline ResNet-50's performance by 2.5% on the Only-FG test set and 1.2% on -ame, showing that MaskTune does not rely much on the background and uses both background and foreground for prediction. On the Mixed-same and Only-FG test sets, MaskTune outperforms other techniques because mixing/removing the background texture/info confuses other methods. These results show that our technique helps to learn task-relevant features without depending on nuisance signal sources.
Table 3: Results from the Background Challenge ImageNet-9 using ResNet-50. Our method outperforms the baselines on both Mixed-same and Only FG test sets. Method
Original
Mixed-same
Mixed-rand
Only-FG
Baseline [Xiao et al., 2020]
96.3
89.8
75.6
85.6
CIM [TTaganakki et al., 2021]
97.7
89.8
81.1
SIN [Sauer and Geiger, 2021]
89.2
73.1
63.7
INSIN [Sauer and Geiger, 2021]
94.7
85.9
78.5
INCGN [Sauer and Geiger, 2021]
94.2
83.4
80.1
MaskTune (Ours)
95.6
91.1
78.6
88.1
4.2 Selective Classification
For selective classification task, we evaluated MaskTune on three datasets: CIFAR-IO [Krizhevsky et al., 20091, SVHN [Netzer et al., 201 I]. and Cats vs. Dogs [Geifman and El-Yaniv, 2019], with coverage values of (90%, 95%, 100%); For example 90% coverage means abstaining 10% of the samples.
Given input image j and the original (migiitia) and finetuned (infinl) models, let their respective inference-tine tineencee-ieeeeeee prediction probabilities for class i be pinit and pfinal Then MaskTune does not abstain and declares the class as the predicted class, iff pinit sj Pf > 7 where y is threshold that allows for controlling the target coverage. To find the proper 7 for achieving the targeted coverage, we iterate over the validation set and find a threshold that, if applied to the validation set, would give the desired coverage (i.e., if our target coverage is 90%, we seek for threshold which if we apply for abstention on the validation set, we will abstain predicting 10% of validation data). Note that the probabilities of the two models multiplied to ensure that as the value of either probability decreases, the possibility of abstention increases.
We compare our method to the other selective classification approaches such as Softmax Response (SR) [Geifman and EI- Yaniv, 2019], SelectiveNet (SN) [Geifman and El- El-vaniv, 2019], Deep Gamblers (DG) [Liu et al., 2019], and One-sided Prediction (OSP) [Gangrade et al., 2021]. Like the OSP, we first split the training data into train and validation sets. After training, we search for the abstention threshold on the validation set, then use it at the test time. As seen in Table 4, MaskTinne outperforms all prior techniques on all three datasets on 95% and 100% coverage rates. For results on more coverage rates refer to Appendix ??.
8

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Table 4: Selective classification results on CIFAR-10, SVHN, and Cats vs. Dogs datasets for different
coverage values.
Target Coverage
SR
SN
DG
OSP
MaskTune
Dataset
Cov.
Err.
Cov.
Err.
Cov.
Err.
Cov.
Err.
Cov.
Err.
100%
99.99 9.58 100
11.07
100
10.81
100 9.74 99.99-10.00
0.94
Cifar-10 10
95%
95.2 8.74 94.7
8.34
95.1
8.21
95.1
6.98 94.86++0.18
6.54++0.39
90%
90.5
6.52
89.6
6.45
90.1
6.14
90.0
4.67 89.73++0222 4.74+0.31
100%
99.97 3.86 100
4.27
100
4.03
100
4.27 100.0= 3.68
SVHN
95%
95.1
1,86
95.1
2.53
95.0
2.05
95.1
1.83 95.19
1.84+0.23
90%
90.0
1.04
90.1
1.31
90.0
1.06
90.1
1.01 89.55
0.96
100%
100 5.72 100
7.36
100
6.16
100
5.93 99.98
4.83
Cats vs. Dogs
95%
95.0
3.46
95.2
5.1
95.1
4.28
95.1
2.97 95.01++0.14
0.15
90%
90.0
2.28
90.2
3.3
90.0
2.5
90.0
1.74 90.78 1.94
5
Related Work
In the following, review the related works which have attempted mitigate the effect of spurious correlation in training deep models. We also discuss previous methods that use attention based online masking and how our method differs.
Robustness to Spurious Correlations. Distributionally robust optimization (DRO) [Ben- Tal et al., 2013, Gao et al., 2017, Duchi et al., 2021] has been proposed to improve generalization to worst cases (minority distributions) in a dataset. However, the DRO objective leads to disproportionate attention to the worst cases, even if they are implausible. To address this issuc, Sagawa al. [2019] proposed to leverage subgroup information during optimization. Although this method reduces the likelihood of the worst-case failure, it is based on prior solid information (i.e., subgroup labels), which is not always available. Several efforts have been made to reduce the subgroup-level supervision. Sohoni et al. [2020] proposed clustering based method for obtaining sub-group information to be used in DRO setup. However, determining the number of clusters (sub-groups) in a dataset is not trivial since small number may still dismiss minor subgroups while a large number may lower overall accuracy. Yaghoobzadeh et al. [2019] proposed measuring sample accuracy throughout training to discover forgettable instances, then tieettunig models using such samples to increase model resilience against spurious correlations. Chen et al. [2020] demonstrated that self- training can also help decrease the effect of spurious features, but only if the source classifier is very accurate and there are not too many isolated sub-groups in the data. Liu et al. [2021] discovered that training a model twice helps it become resistant to spurious correlation. However, if model reaches high classification accuracy in the first run, this technique is no longer useful since there is not enough misclassified data to retrain the model. MaskTune, on the other hand, employs an orthogonal approach learn robust representations through gradient-based masking and fine-tuning and does not require subgroup-level labels during training or model selection.
Online Attention-based Masking. A large number of studies suggest using attention (soft) masks eliminate irrelevant information from input data during training [Sharma al., 2015, Wang et al., 2017, Xu et al., 2015, Zheng et al., 2017]. Although these methods increase overall classification accuracy, they are incapable of disregarding spurious correlations since attention may be on an area of the input where the spurious correlations are dominant during training. For concentrating attention only on the foreground, Li et al. [2018] developed guided attention model. Their technique, however, needs additional annotations of object docations/masks, Nonetheless, none of these methods aimed to reduce the effects of spurious correlations. Even with complete supervision (i.e., masking the whole background), spurious correlations might still occur in the foreground. MaskTune, on the other hand, produces masks from fully trained model and uses them for a single epoch fine-tuning. MaskTune does not require any additional supervision such object location annotations.

---

6 Conclusion
In this work, we considered the problem of preventing models from learning spurious correlations. We introduced a new fine-tuning technique that is designed explicitly for spurious correlations. It enforces a model to explore more variables in the input and map them the same target. Through experiments and theoretical analysis on classification with nuisance background information which typically suffers from the presence of spurious correlations in the data, we showed that models trained with MaskTume outperform previous relevant methods. We also showed that MaskTune helps to improve the accuracy significantly selective classification tasks. Adapting MaskTune for non-image data, such as sentiment analysis, can be an intriguing future work. Another area for future work is how best to identify and use the most effective masked samples (rather than all) when fine-tuning the final model, perhaps using uncertainty information or active learning. Although MaskTune can help reduce the effect of many types of spurious correlation, such as texture, color, and localized nuisance features e.g., artifacts added to x-ray images by medical imaging devices, there are some cases where MaskTune may not be effective, such as a small transformation all pixel values for some of the images in dataset. This occurs in medical devices or cameras that add almost imperceptible color (values) to captured images.
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1. For all authors...
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13


Reviews:
[{"review_id": "u3mjW_Q9QIH", "reviewer": "Reviewer_5uk4", "summary": "The authors propose a two-stage pipeline ('MaskTune') for mitigating spurious correlations where the the first stage simply entails training a convolutional model via ERM. GradCAM is is then applied to the resulting model to generate salient maps; these are converted into masks by thresholding and used to mask out the inputs during the second stage wherein the model is fine-tuned for a single epoch, the goal being to force the model to  explore alternative views of the data.  Experiments are preformed on a range of image dataset for both regular classification and selective classification, in which the option to abstain from predicting is added.  The authors propose to ensemble the models obtained from the first and second stages of training to obtain the probabilities used for the latter task, optimising the threshold to meet the desired coverage using a validation set. The proposed method performs competitively with other recent domain generalisation methods without needing the groups/environments/domains to be annotated, as is the case for popular methods such as GDRO.\n", "questions": "- How robust is the chosen thresholding heuristic? Do you expect it to hold in general and can you suggest a more rigorous approach to setting it for if not (one which is based on something more concrete than avoiding masking too many/too few variables)?\n- Is a two-stage process, whereby spurious features are identified based on only a single round of training, sufficient in general to avoid the learning of spurious correlations? It seems that a dataset consisting of multiple sets of features that spuriously correlate with the target (for instance, background colour and background texture) would pose problems for MaskTune.", "limitations": "I am satisfied with the extent to which the societal impacts of the method are addressed -- the interpretability of the work could be highlighted to further bolster the paper in this respect. The authors address a couple of limitations of the method -- it's unimodality and sample inefficiency -- at the end of the conclusion, however I would like to see the the discussion on this topic expanded, both with regard to those limitations already noted and in the regard to addressing some of those concerns such as those raised in \"Strengths are Weaknesses\"/\"Questions\" (or to see relevant ablations provided if in fact those concerns are in fact unjustified)", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "# Strengths\n- Despite the similarity in spirit to pre-existing method (which are appropriately cited) the method nonetheless appears to be novel and is appealing for  its relative simplicity compared with other domain generalisation methods.\nThat said, I worry that it might be too brittle to be practically useful, for several reasons:\n1) From what I gather, the efficacy of the method is highly dependent on the choice of masking threshold, $\\lambda$ -- choosing a value that is too low would allow for leakage of the spurious features, while, as discussed in the paper, choosing a value that is too low leads to portions of the samples being masked out, including those salient features which we hope to learn during the fine-tuning phase. The authors propose to mask using a heuristic based on (standard) deviations from the mean, however the justification for it feels a bit hand-wavy. Moreover, the method relies heavily on the efficacy of the mask-generation method and it seems artefacts induced by upscaling (which may be significant depending on the choice of architecture and the input resolution) could prove detrimental. \n2) It seems likely that in many cases several a single iteration of the method would be insufficient, as -- to continue with the motivating example adduced in the paper -- one can imagine only a portion of the background features being needed to reliably distinguish between cows and camels in the training set, resulting in other background features remaining unmasked during fine-tuning. One could repeat the procedure until some stopping criterion is reached but it's not obvious whether problems such as catastrophic forgetting begin to factor into the optimisation process, hindering the efficacy of MaskTune. \n3) If there are no spurious correlations in the training set and the set of unmasked features contain no information predictive of the class label there may be risk of an over-parameterised model resorting to memorisation -- that we only fine-tune for a single epoch may mean that this typically isn't a problem, but there nonetheless seems like such a risk is there with the current incarnation of the algorithm.\n4) Dovetailing from the previous point, a single epoch of fine-tuning may not be a sufficient duration for learning more complex sets of features/unlearning the bias learned during the first stage of training, yet training training for longer may also carry its share of problems (such as memorisation/catastrophic forgetting).\nThe advantage of the method, however, is that the above problems can, in theory, be readily identified -- this demands an auditing process however, one that is likely to be time-consuming, especially if multiple iterations are required\n\n - Figure 1 provides a straightforward illustration of the process, however it would be useful to have a legend \n denoting the meaning of different mask colourings.\n\n- Comparison with other works is sufficiently thorough -- highlighting the distinction between the proposed method\nand Just train Twice (JtT), which adopts a similar two-stage approach (and how the latter can fail) being particularly pertinent.\n\n- Good range of datasets and a good number of relevant baselines which cover both standard classification and selective classification,\n- While currently listed as an avenue of future work, I think how one MaskTune might be concretely adapted to other\ntasks (e.g, segmentation) and modalities warrants some discussion.\n\n- Indications  table indicating what sources of supervision are required for the methods run on the Waterbirds dataset is nice to see\n but exactly what is meant by model-selection supervision isn't explained ('Train Supervision' should also perhaps be indicated to be w.r.t. the group labels) -- it's meaning can be inferred but I don't think it's self-evident to forgo explanation (even if only at the caption level_.\n\n- The paper is for the most part easily understandable and soundly structured. However there are some areas of the text which could be clearer; the area which perhaps stood out the most in this respect was that relating to the biased MNIST setup, with the sentence \"We place 99% and 1% of all digits labelled \"0-4\" and \"5-9\" in the training  set on a background with a small blue square on the top left corner, respectively, and keep the remaining data intact\" being quite difficult parse. On the topic of this section, it would also be useful to have Figure 2 include a visual description of the two setups considered.\n\n- Error intervals (whether these correspond to standard error or deviation is not stated) are given\nfor only a subset of the methods and datasets (none of the results for selective classification \nhave them, for instance). \n\n- I found it odd that in explaining the ensembling and threshold-selection procedure for selective classification\n(paragraph beginning line 241) the authors choose to introduce new notation for the initial and final models when\nnotations different from that established in Section 2 of the paper.\n", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "FOUVKiMGol0", "reviewer": "Reviewer_eFV8", "summary": "This paper proposes a simple technique to avoid reliance on \"spurious correlations\": 1) train a model, 2) fine-tune for an additional epoch, but, mask the part of the data that is deemed important for making the predictions. This forces the classifier to \"explore\" and learn something beyond the initial \"spurious correlation\". Results on e.g., Waterbirds and CelebA, the method outperform other methods which don't use group information.", "questions": "I do not expect that all of these questions are answered.\n- What happens in the synthetic MNIST experiment when there are two spurious correlations. E.g., two squares instead of one?\n- Why only fine-tune for one epoch? What happens when you do more?\n- Does MaskTune help on natural distribution shifts such as ImageNet -> ImageNetV2.\n- What happens when there is no spurious correlations, does MaskTune reduce accuracy? What happens if you run MaskTune on the benchmark ImageNet dataset.", "limitations": "This is marked as Yes in the checklist but a limitations section could be appreciated in the revision.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\nThe method is straightforward and, for the most part, very intuitive. Moreover, the performance is validated empirically on multiple datasets, where MaskTune (the method) outperforms other methods which do not have access to group information and approaches the performance of methods which do.\n\nWeaknesses:\nThe main concern is that this method is only tested on datasets where spurious correlations exist. E.g., I am not surprised the MNIST case works. My concern is that, if I don't know apriori that spurious correlations exist, should I apply MaskTune or not? Are spurious correlations really a problem on datasets like ImageNet? Currently it is unclear if I should apply this method when training on ImageNet. It is even possible that MaskTune could reduce accuracy when training on datasets without spurious correlations, as the important features could be masked out.", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "4oGdQ8-P8ZZ", "reviewer": "Reviewer_tJb1", "summary": "This paper focuses on the important problem of mitigating reliance on spurious correlations. In particular, the paper proposes MaskTune, a masking strategy to prevent over-reliance on spurious correlations. The masking strategy forces the model to reduce relaince on spurious correlations and learn new features via the masked examples. The empirical results show that MaskTune improve model performance on multiple datasets and has applications to the task of selective classification. ", "questions": "Please see strengths and weaknesses", "limitations": "Please see strengths and weaknesses", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\n- Well-written: The problem is well-motivated, figure 1 does a good job at describing the masking approach, the experiment setup, datasets, and results are easy to follow.\n- The proposed masking approach to spread reliance across multiple features is conceptually simple + effective and does not require knowledge of contextual / background information.\n- The empirical results on mitigating spurious correlations are significantly better than existing unsupervised methods when you do not have access to supervision at model selection time (a more realistic setting than what has been considered previously)\n- The connection between spurious correlations (narrow subset of features) and selective classification is quite interesting; the paper presents initial theoretical results in the overparameterized linear regression setup and the ensemble idea is empirically better than three existing methods on three datasets.\n\n**Weaknesses**\n\n- Baselines: the randmask baseline is interesting but quite underutilized in the experiments on real datasets. Also, the paper does not provide any details about this method (e.g., how aggressive is the random cropping? how many copies of the same image with different random masks are used?).  The gap between randmask and masktune seems a bit too significant even for the toy MNIST dataset and I am wondering if a stronger version of randmask can be used in these experiments.\n- Saliency maps: The masktune approach heavily relies on explanation / saliency maps working “as expected”. However, feature attribution methods in general are hard to evaluate and work on evaluation of interpretability methods have shown that these methods have major failure modes.  There is work [R1, R2, R3] that indicate that saliency maps are ineffective at detecting (and so mitigating) spurious correlations. A discussion of this issue and about the usage of a specific saliency map method (xGradCAM) is missing. In particular, how robust are these results to a different choice of explaination method (on realistic datasets such celebA and waterbirds)?\n- Spurious correlations and locality: it seems like this method would only work in cases where the spurious correlation can be localized in input space. That is, the spurious correlation “region” in the image is different from the “region” of the desirable / core feature. For example, in the MNIST experiment, if instead of adding a patch, I add a red vs. blue tint to the entire image, the model will just learn the color feature. Will masktune mitigate shortcut learning in this case? It would be great if the authors can shed light on this.\n- Are the number in table 3 and 4 averaged over multiple runs or is it best-of-K? are the differences / improvements statistically significant?\n\n[R1] Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation\n\n[R2] Do Input Gradients Highlight Discriminative Features?\n\n[R3] Sanity Simulations for Saliency Methods", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "2POs4tIptwA", "reviewer": "Reviewer_dGKz", "summary": "Spurious learning or shortcut learning problem has received increasing attention from the community recently. This work suggests the masking approach MaskTune, in order to avoid relying too heavily on spurious (or a small number of) features. More specifically, MaskTune forces the trained model to explore new features during a single epoch finetuning by masking previously discovered features. Experimental results show that the proposed method could improve the worst-group accuracy for spurious learning tasks. In addition, it also achieves decent performance for the selection classification task.\n", "questions": "Please refer to the Strengths And Weaknesses part", "limitations": "Yes", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Advantages:\n1. Spurious correlation is a problem worth studying. The idea of encouraging the model to explore new features is sound.\n2. The paper is overall well written.\n3. Experimental results validate that the proposed method can be used to alleviate the reliance on spurious correlations and be applied in the selection classification task.\n  \n\nSome minor concerns:\n1. The positions of sections 3 and 4 should be switched. Implementations should be introduced before experimental results.\n2. Experimental evaluation is only performed using toy datasets, such as MNIST and CelebA. It is unclear whether the proposed method could be applied in more challenging real-world datasets such as those from the WILDS benchmark [1].\n3. The writing can be improved. In the abstract and the introduction section, the authors mentioned the selection classification task and did not give too much introduction to this task. Only after reading Section 2.2, the readers can understand what is the selection classification task. This could be unfriendly for readers not familiar with this task.\n4. On line 107, the authors mention that one major benefit of using xGradCAM is that it could produce dense heatmaps. What would happen if sparse heatmaps are generated?\n\n[1] Koh, Pang Wei, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu et al. \"Wilds: A benchmark of in-the-wild distribution shifts.\" In International Conference on Machine Learning, pp. 5637-5664. PMLR, 2021. \n", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept