On Feature Learning in the Presence of Spurious Correlations
- URL: http://arxiv.org/abs/2210.11369v1
- Date: Thu, 20 Oct 2022 16:10:28 GMT
- Title: On Feature Learning in the Presence of Spurious Correlations
- Authors: Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
- Abstract summary: We show that the quality learned feature representations is greatly affected by the design decisions beyond the method.
We significantly improve upon the best results reported in the literature on the popular Waterbirds, Celeb hair color prediction and WILDS-FMOW problems.
- Score: 45.86963293019703
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep classifiers are known to rely on spurious features $\unicode{x2013}$
patterns which are correlated with the target on the training data but not
inherently relevant to the learning problem, such as the image backgrounds when
classifying the foregrounds. In this paper we evaluate the amount of
information about the core (non-spurious) features that can be decoded from the
representations learned by standard empirical risk minimization (ERM) and
specialized group robustness training. Following recent work on Deep Feature
Reweighting (DFR), we evaluate the feature representations by re-training the
last layer of the model on a held-out set where the spurious correlation is
broken. On multiple vision and NLP problems, we show that the features learned
by simple ERM are highly competitive with the features learned by specialized
group robustness methods targeted at reducing the effect of spurious
correlations. Moreover, we show that the quality of learned feature
representations is greatly affected by the design decisions beyond the training
method, such as the model architecture and pre-training strategy. On the other
hand, we find that strong regularization is not necessary for learning high
quality feature representations. Finally, using insights from our analysis, we
significantly improve upon the best results reported in the literature on the
popular Waterbirds, CelebA hair color prediction and WILDS-FMOW problems,
achieving 97%, 92% and 50% worst-group accuracies, respectively.
Related papers
- Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly.
Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness.
Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings.
This paper introduces an innovative multi-modal prompt-based methodology for IQA.
arXiv Detail & Related papers (2024-04-23T11:45:32Z) - TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation [1.5836776102398225]
Long-tailed distribution of entities of KG and noise issues in the real world make item-entity dependent relations deviate from reflecting true characteristics.
We design the Two-Level Debiased Contrastive Learning (TDCL) and deploy it in the knowledge graph.
Considerable experiments on open-source datasets demonstrate that our method has excellent anti-noise capability.
arXiv Detail & Related papers (2023-10-01T03:56:38Z) - Robust Learning with Progressive Data Expansion Against Spurious
Correlation [65.83104529677234]
We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features.
Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process.
We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
arXiv Detail & Related papers (2023-06-08T05:44:06Z) - Learning Deep Representations via Contrastive Learning for Instance
Retrieval [11.736450745549792]
This paper makes the first attempt that tackles the problem using instance-discrimination based contrastive learning (CL)
In this work, we approach this problem by exploring the capability of deriving discriminative representations from pre-trained and fine-tuned CL models.
arXiv Detail & Related papers (2022-09-28T04:36:34Z) - CONVIQT: Contrastive Video Quality Estimator [63.749184706461826]
Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms.
Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner.
Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.
arXiv Detail & Related papers (2022-06-29T15:22:01Z) - Task-Agnostic Robust Representation Learning [31.818269301504564]
We study the problem of robust representation learning with unlabeled data in a task-agnostic manner.
We derive an upper bound on the adversarial loss of a prediction model on any downstream task, using its loss on the clean data and a robustness regularizer.
Our method achieves preferable adversarial performance compared to relevant baselines.
arXiv Detail & Related papers (2022-03-15T02:05:11Z) - Accurate and Robust Feature Importance Estimation under Distribution
Shifts [49.58991359544005]
PRoFILE is a novel feature importance estimation method.
We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
arXiv Detail & Related papers (2020-09-30T05:29:01Z) - Precise Tradeoffs in Adversarial Training for Linear Regression [55.764306209771405]
We provide a precise and comprehensive understanding of the role of adversarial training in the context of linear regression with Gaussian features.
We precisely characterize the standard/robust accuracy and the corresponding tradeoff achieved by a contemporary mini-max adversarial training approach.
Our theory for adversarial training algorithms also facilitates the rigorous study of how a variety of factors (size and quality of training data, model overparametrization etc.) affect the tradeoff between these two competing accuracies.
arXiv Detail & Related papers (2020-02-24T19:01:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.