Question: In the experiments, in all three settings, the training and test distributions are intentionally different, so that the models that have undesirable working mechanisms, e.g. relying on spurious correlation, are "defined" to be bad. Is this a characterization of the datasets used?
Evidence: Modern pretrained models can be adapted to new tasks with remarkably little data, enabling downstream applications for tasks with only tens or hundreds of examples. However, an important but neglected challenge that is especially salient in few-shot settings is task ambiguity, when the desired behavior is not uniquely specified by the provided examples. Task ambiguity can manifest in different ways: For example, the class-relevant features of an input (e.g., the shape of an object in Figure) may be spuriously correlated with other features predictive of the training labels (e.g., the color of the object), making the desired task unclear. In addition, task ambiguity may arise from an underdiverse training set, causing models to be unsure of the desired behavior for minority groups or during distribution shifts that occur at test-time.
We consider whether the task ambiguity problem can be addressed through active learning, where models select informative examples for users to label. In principle, active learning allows models themselves to assist in resolving task ambiguity by identifying examples whose labels would be informative; for example, in fig., asking for the label of the blue square helps determine that the desired task is to predict the object shape not the object color. Since it may be challenging in general for users to intuit possible sources of task ambiguity, much less how to address them with example selection, an automated active learning approach may be desirable.
In traditional settings with smaller, unpretrained models, several challenges often prevent active learning from seeing success in practice, including label noise, unlearnable examples, and a lack of generalizability across models and tasks. However, using pretrained models in few-shot or low-data settings has several key differences from traditional ML pipelines which may make them particularly well-suited for active learning. First, the effect of an individual data point (and the difference between an informative vs. an uninformative one) is magnified, compared to the thousands or millions of examples typically acquired through standard active learning pipelines. Second, pretrained models excel at learning high-level representations of inputs, which better surface relevant features and may encourage active learning to select examples that disambiguate between these high-level features (e.g. shape and color).
We consider the use of active learning on a range of spuriously correlated, imbalanced, or domain shifted datasets where task ambiguity is salient. We compare an active learning approach with a random-sampling baseline, and compare the difference in performance with and without the use of pretrained models. Our contributions are:
1. Identifying and motivating task ambiguity as a unified concern across a range of real-world datasets, and an especially salient problem for low-data settings.
2. Proposing active learning as a potential solution, along with a simple yet practical recipe that does not require adjustment for new datasets or tuning on validation data.
3. Showing that active learning can enable large gains (up to 6× reduction in data points, +12% absolute gain for the same labeling budget) and presenting scaling trends demonstrating that this is an emergent property of pretraining.
Spurious correlations arise when multiple features are predictive of the label in a training dataset, yet it is ambiguous which ones are causally linked to the task label. We consider two such datasets, and see whether active learning can choose the disambiguating examples where the spurious features are not copresent with the causal features:
Waterbirds The Waterbirds dataset consists of photographs of landbirds or waterbirds digitally edited onto land or water backgrounds. The task is to classify whether the bird is a landbird or a waterbird. In the train set, 77% of the pictures feature landbirds and 23% waterbirds. 95% of both landbirds and waterbirds appear on land and water backgrounds, respectively. In the validation and test sets, this percentage is decreased to 50%, instead. Thus, the image background is a spurious feature the model may come to rely on when making the prediction.
Treeperson As the Waterbirds dataset was synthetically generated, we also consider a dataset where we perform classification over real, unedited images with spuriously correlated objects. We use the object annotations in Visual Genome to create a new dataset of 8,638 images called Treeperson, for which the task is to predict whether a person is in a given image. While 50% of the images contain a person in this dataset, each image also contains either a tree or a building, and the presence of these objects is spuriously correlated with the presence of people. At train time, 90% of training images with people contain a building, while 90% of training images without people contain a tree. Thus, a model may be incentivized to form representations that classify according to the presence of trees and buildings, rather than the presence of the actual causal variable of interest (people). These values are changed to 50% at test time, removing this correlation to evaluate how well the model learned the actual feature of interest. For more details on this dataset, see Appendix C.
Spurious correlations arise when multiple features are predictive of the label in a training dataset, yet it is ambiguous which ones are causally linked to the task label. We consider two such datasets, and see whether active learning can choose the disambiguating examples where the spurious features are not copresent with the causal features:
Waterbirds The Waterbirds dataset consists of photographs of landbirds or waterbirds digitally edited onto land or water backgrounds. The task is to classify whether the bird is a landbird or a waterbird. In the train set, 77% of the pictures feature landbirds and 23% waterbirds. 95% of both landbirds and waterbirds appear on land and water backgrounds, respectively. In the validation and test sets, this percentage is decreased to 50%, instead. Thus, the image background is a spurious feature the model may come to rely on when making the prediction.
Treeperson As the Waterbirds dataset was synthetically generated, we also consider a dataset where we perform classification over real, unedited images with spuriously correlated objects. We use the object annotations in Visual Genome to create a new dataset of 8,638 images called Treeperson, for which the task is to predict whether a person is in a given image. While 50% of the images contain a person in this dataset, each image also contains either a tree or a building, and the presence of these objects is spuriously correlated with the presence of people. At train time, 90% of training images with people contain a building, while 90% of training images without people contain a tree. Thus, a model may be incentivized to form representations that classify according to the presence of trees and buildings, rather than the presence of the actual causal variable of interest (people). These values are changed to 50% at test time, removing this correlation to evaluate how well the model learned the actual feature of interest. For more details on this dataset, see Appendix C.
Distribution shifts occur when algorithms are evaluated on different data distributions than the ones they were trained on. Examples include changing the location or time of day that photos were taken, or changing the topic or author of a particular textual source. These shifts can reduce performance, and we consider whether active learning can help choose diverse, informative examples that clarify how the model should behave over a range of natural distribution shifts.
iWildCam2020-WILDS This dataset considers the task of species classification from a database of photos taken from wildlife camera traps. The dataset is unbalanced, with most images containing no animal, and the distribution of camera locations and species changes between the in domain (ID) and out-of-domain (OOD) subsets.
Amazon-WILDS This dataset considers the task of predicting the number of stars associated with the text of a given Amazon review. The reviewers are different in the training set versus the test set, and the task is to perform as well as possible on this set of new reviewers. In addition to number of stars, we also consider model performance stratified by different product types, which highlights minority subgroups whose categorization is not visible to the model.
Distribution shifts occur when algorithms are evaluated on different data distributions than the ones they were trained on. Examples include changing the location or time of day that photos were taken, or changing the topic or author of a particular textual source. These shifts can reduce performance, and we consider whether active learning can help choose diverse, informative examples that clarify how the model should behave over a range of natural distribution shifts.
iWildCam2020-WILDS This dataset considers the task of species classification from a database of photos taken from wildlife camera traps. The dataset is unbalanced, with most images containing no animal, and the distribution of camera locations and species changes between the in domain (ID) and out-of-domain (OOD) subsets.
Amazon-WILDS This dataset considers the task of predicting the number of stars associated with the text of a given Amazon review. The reviewers are different in the training set versus the test set, and the task is to perform as well as possible on this set of new reviewers. In addition to number of stars, we also consider model performance stratified by different product types, which highlights minority subgroups whose categorization is not visible to the model.
Distribution shifts occur when algorithms are evaluated on different data distributions than the ones they were trained on. Examples include changing the location or time of day that photos were taken, or changing the topic or author of a particular textual source. These shifts can reduce performance, and we consider whether active learning can help choose diverse, informative examples that clarify how the model should behave over a range of natural distribution shifts.
iWildCam2020-WILDS This dataset considers the task of species classification from a database of photos taken from wildlife camera traps. The dataset is unbalanced, with most images containing no animal, and the distribution of camera locations and species changes between the in domain (ID) and out-of-domain (OOD) subsets.
Amazon-WILDS This dataset considers the task of predicting the number of stars associated with the text of a given Amazon review. The reviewers are different in the training set versus the test set, and the task is to perform as well as possible on this set of new reviewers. In addition to number of stars, we also consider model performance stratified by different product types, which highlights minority subgroups whose categorization is not visible to the model.
We consider whether the task ambiguity problem can be addressed through active learning, where models select informative examples for users to label. ****We consider two such datasets, and see whether active learning can choose the disambiguating examples where the spurious features are not copresent with the causal features: Waterbirds The Waterbirds dataset (Sagawa et al., 2019) consists of photographs of landbirds or waterbirds digitally edited onto land or water backgrounds.****Treeperson As the Waterbirds dataset was synthetically generated, we also consider a dataset where we perform classification over real, unedited images with spuriously correlated objects.****Distribution shifts occur when algorithms are evaluated on different data distributions than the ones they were trained on.****iWildCam2020-WILDS This dataset considers the task of species classification from a database of photos taken from wildlife camera traps (Beery et al., 2020; Koh et al., 2021).****Amazon-WILDS This dataset considers the task of predicting the number of stars associated with the text of a given Amazon review (Ni et al., 2019; Koh et al., 2021).
Answer: Yes! Our core focus is active learning as a means to resolve task ambiguity. Real world datasets exhibit a range of phenomena that make specifying the desired task challenging (e.g. spurious correlations, latent subgroups, etc). We conduct experiments on four diverse datasets where task ambiguity is salient to test whether active learning helps the model acquire the correct behavior.