How to Construct Perfect and Worse-than-Coin-Flip Spoofing Countermeasures: A Word of Warning on Shortcut Learning

• URL: http://arxiv.org/abs/2306.00044v1
• Date: Wed, 31 May 2023 15:58:37 GMT
• Title: How to Construct Perfect and Worse-than-Coin-Flip Spoofing Countermeasures: A Word of Warning on Shortcut Learning
• Authors: Hye-jin Shim, Rosa Gonz\'alez Hautam\"aki, Md Sahidullah, Tomi Kinnunen
• Abstract summary: Shortcut learning, or Clever Hans effect refers to situations where a learning agent learns spurious correlations present in data, resulting in biased models. We focus on finding shortcuts in deep learning based spoofing countermeasures (CMs) that predict whether a given utterance is spoofed or not.
• Score: 20.486639064376014
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Shortcut learning, or `Clever Hans effect` refers to situations where a learning agent (e.g., deep neural networks) learns spurious correlations present in data, resulting in biased models. We focus on finding shortcuts in deep learning based spoofing countermeasures (CMs) that predict whether a given utterance is spoofed or not. While prior work has addressed specific data artifacts, such as silence, no general normative framework has been explored for analyzing shortcut learning in CMs. In this study, we propose a generic approach to identifying shortcuts by introducing systematic interventions on the training and test sides, including the boundary cases of `near-perfect` and `worse than coin flip` (label flip). By using three different models, ranging from classic to state-of-the-art, we demonstrate the presence of shortcut learning in five simulated conditions. We analyze the results using a regression model to understand how biases affect the class-conditional score statistics.

Related papers

• Probably Approximately Precision and Recall Learning [62.912015491907994]
  Precision and Recall are foundational metrics in machine learning. One-sided feedback--where only positive examples are observed during training--is inherent in many practical problems. We introduce a PAC learning framework where each hypothesis is represented by a graph, with edges indicating positive interactions.
  arXiv  Detail & Related papers  (2024-11-20T04:21:07Z)
• Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding [5.4480125359160265]
  We propose a pessimistically aggregating the predictions of a mixture-of-experts, assuming each expert captures relatively different latent features. The experimental results demonstrate that our post-hoc control over the experts significantly enhances the model's robustness to the distribution shift in shortcuts.
  arXiv  Detail & Related papers  (2024-06-17T20:00:04Z)
• Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery [56.172872410834664]
  Generalized category discovery (GCD) aims at addressing a more realistic and challenging setting of semi-supervised learning. We propose a Memory Consistency guided Divide-and-conquer Learning framework (MCDL) Our method outperforms state-of-the-art models by a large margin on both seen and unseen classes of the generic image recognition.
  arXiv  Detail & Related papers  (2024-01-24T09:39:45Z)
• On the Foundations of Shortcut Learning [20.53986437152018]
  We study how predictivity and availability interact to shape models' feature use. We find that linear models are relatively unbiased, but introducing a single hidden layer with ReLU or Tanh units yields a bias.
  arXiv  Detail & Related papers  (2023-10-24T22:54:05Z)
• Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention [72.12974259966592]
  We present a unique and systematic study of a temporal bias due to frame length discrepancy between training and test sets of trimmed video clips. We propose a causal debiasing approach and perform extensive experiments and ablation studies on the Epic-Kitchens-100, YouCook2, and MSR-VTT datasets.
  arXiv  Detail & Related papers  (2023-09-17T15:58:27Z)
• Monitoring Shortcut Learning using Mutual Information [16.17600110257266]
  Shortcut learning is evaluated on real-world data that does not contain spurious correlations. Experiments demonstrate that MI can be used as a metric network shortcut network.
  arXiv  Detail & Related papers  (2022-06-27T03:55:23Z)
• Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation [44.319739489968164]
  Deep neural networks often take dataset biases as a shortcut to make decisions rather than understand tasks. In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution. We propose a training strategy Less-Learn-Shortcut (LLS): our strategy quantifies the biased degree of the biased examples and down-weights them accordingly.
  arXiv  Detail & Related papers  (2022-05-25T09:08:35Z)
• Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models [53.36605766266518]
  We show that trained NLU models have strong preference for features located at the head of the long-tailed distribution. We propose a shortcut mitigation framework, to suppress the model from making overconfident predictions for samples with large shortcut degree.
  arXiv  Detail & Related papers  (2021-03-11T19:39:56Z)
• A framework for predicting, interpreting, and improving Learning Outcomes [0.0]
  We develop an Embibe Score Quotient model (ESQ) to predict test scores based on observed academic, behavioral and test-taking features of a student. ESQ can be used to predict the future scoring potential of a student as well as offer personalized learning nudges.
  arXiv  Detail & Related papers  (2020-10-06T11:22:27Z)
• Open-set Short Utterance Forensic Speaker Verification using Teacher-Student Network with Explicit Inductive Bias [59.788358876316295]
  We propose a pipeline solution to improve speaker verification on a small actual forensic field dataset. By leveraging large-scale out-of-domain datasets, a knowledge distillation based objective function is proposed for teacher-student learning. We show that the proposed objective function can efficiently improve the performance of teacher-student learning on short utterances.
  arXiv  Detail & Related papers  (2020-09-21T00:58:40Z)
• 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.