WASP: A Weight-Space Approach to Detecting Learned Spuriousness
- URL: http://arxiv.org/abs/2410.18970v3
- Date: Thu, 13 Feb 2025 17:57:28 GMT
- Title: WASP: A Weight-Space Approach to Detecting Learned Spuriousness
- Authors: Cristian Daniel Păduraru, Antonio Bărbălau, Radu Filipescu, Andrei Liviu Nicolicioiu, Elena Burceanu,
- Abstract summary: We propose a method that switches the focus from analyzing a model's predictions to analyzing the model's weights.
Our proposed Weight-space Approach to detecting Spuriousness (WASP) relies on analyzing the weights of foundation models as they drift towards capturing various (spurious) correlations.
We demonstrate that different from previous works, our method can expose spurious correlations featured by a dataset even when they are not exposed by training or validation counterexamples.
- Score: 5.025665239455297
- License:
- Abstract: It is of crucial importance to train machine learning models such that they clearly understand what defines each class in a given task. Though there is a sum of works dedicated to identifying the spurious correlations featured by a dataset that may impact the model's understanding of the classes, all current approaches rely solely on data or error analysis. That is, they cannot point out spurious correlations learned by the model that are not already pointed out by the counterexamples featured in the validation or training sets. We propose a method that transcends this limitation, switching the focus from analyzing a model's predictions to analyzing the model's weights, the mechanism behind the making of the decisions, which proves to be more insightful. Our proposed Weight-space Approach to detecting Spuriousness (WASP) relies on analyzing the weights of foundation models as they drift towards capturing various (spurious) correlations while being fine-tuned on a given dataset. We demonstrate that different from previous works, our method (i) can expose spurious correlations featured by a dataset even when they are not exposed by training or validation counterexamples, (ii) it works for multiple modalities such as image and text, and (iii) it can uncover previously untapped spurious correlations learned by ImageNet-1k classifiers.
Related papers
- DISCO: DISCovering Overfittings as Causal Rules for Text Classification Models [6.369258625916601]
Post-hoc interpretability methods fail to capture the models' decision-making process fully.
Our paper introduces DISCO, a novel method for discovering global, rule-based explanations.
DISCO supports interactive explanations, enabling human inspectors to distinguish spurious causes in the rule-based output.
arXiv Detail & Related papers (2024-11-07T12:12:44Z) - Spuriousness-Aware Meta-Learning for Learning Robust Classifiers [26.544938760265136]
Spurious correlations are brittle associations between certain attributes of inputs and target variables.
Deep image classifiers often leverage them for predictions, leading to poor generalization on the data where the correlations do not hold.
Mitigating the impact of spurious correlations is crucial towards robust model generalization, but it often requires annotations of the spurious correlations in data.
arXiv Detail & Related papers (2024-06-15T21:41:25Z) - Stubborn Lexical Bias in Data and Models [50.79738900885665]
We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data.
We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations.
Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
arXiv Detail & Related papers (2023-06-03T20:12:27Z) - A Closer Look at Few-shot Classification Again [68.44963578735877]
Few-shot classification consists of a training phase and an adaptation phase.
We empirically prove that the training algorithm and the adaptation algorithm can be completely disentangled.
Our meta-analysis for each phase reveals several interesting insights that may help better understand key aspects of few-shot classification.
arXiv Detail & Related papers (2023-01-28T16:42:05Z) - Influence Tuning: Demoting Spurious Correlations via Instance
Attribution and Instance-Driven Updates [26.527311287924995]
influence tuning can help deconfounding the model from spurious patterns in data.
We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data.
arXiv Detail & Related papers (2021-10-07T06:59:46Z) - Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles [66.15398165275926]
We propose a method that can automatically detect and ignore dataset-specific patterns, which we call dataset biases.
Our method trains a lower capacity model in an ensemble with a higher capacity model.
We show improvement in all settings, including a 10 point gain on the visual question answering dataset.
arXiv Detail & Related papers (2020-11-07T22:20:03Z) - Few-shot Visual Reasoning with Meta-analogical Contrastive Learning [141.2562447971]
We propose to solve a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning.
We extract structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning.
We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce.
arXiv Detail & Related papers (2020-07-23T14:00:34Z) - Learning Causal Models Online [103.87959747047158]
Predictive models can rely on spurious correlations in the data for making predictions.
One solution for achieving strong generalization is to incorporate causal structures in the models.
We propose an online algorithm that continually detects and removes spurious features.
arXiv Detail & Related papers (2020-06-12T20:49:20Z) - Learning What Makes a Difference from Counterfactual Examples and
Gradient Supervision [57.14468881854616]
We propose an auxiliary training objective that improves the generalization capabilities of neural networks.
We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task.
Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
arXiv Detail & Related papers (2020-04-20T02:47:49Z)
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.