White Paper Assistance: A Step Forward Beyond the Shortcut Learning

• URL: http://arxiv.org/abs/2106.04178v1
• Date: Tue, 8 Jun 2021 08:35:44 GMT
• Title: White Paper Assistance: A Step Forward Beyond the Shortcut Learning
• Authors: Xuan Cheng, Tianshu Xie, Xiaomin Wang, Jiali Deng, Minghui Liu, Ming Liu
• Abstract summary: We show that CNNs often overlook the need to examine whether they are doing the way we are actually interested. We propose a novel approach called White Paper Assistance to combat this unintended propensity. Our proposed method involves the white paper to detect the extent to which the model has preference for certain characterized patterns and alleviates it by forcing the model to make a random guess on the white paper.
• Score: 6.066543113636522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested. We show through experiments that even over-parameterized models would still solve a dataset by recklessly leveraging spurious correlations, or so-called 'shortcuts'. To combat with this unintended propensity, we borrow the idea of printer test page and propose a novel approach called White Paper Assistance. Our proposed method involves the white paper to detect the extent to which the model has preference for certain characterized patterns and alleviates it by forcing the model to make a random guess on the white paper. We show the consistent accuracy improvements that are manifest in various architectures, datasets and combinations with other techniques. Experiments have also demonstrated the versatility of our approach on fine-grained recognition, imbalanced classification and robustness to corruptions.

Related papers

• ConceptDrift: Uncovering Biases through the Lens of Foundational Models [5.025665239455297]
  We propose ConceptDrift, a method which analyzes the weights of a linear probe, learned on top a foundational model. We empirically prove the efficacy of our method, by significantly improving zero-shot performance with biased-augmented prompting.
  arXiv  Detail & Related papers  (2024-10-24T17:59:16Z)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods. We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results. We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• A Fixed-Point Approach to Unified Prompt-Based Counting [51.20608895374113]
  This paper aims to establish a comprehensive prompt-based counting framework capable of generating density maps for objects indicated by various prompt types, such as box, point, and text. Our model excels in prominent class-agnostic datasets and exhibits superior performance in cross-dataset adaptation tasks.
  arXiv  Detail & Related papers  (2024-03-15T12:05:44Z)
• Fairness Without Harm: An Influence-Guided Active Sampling Approach [32.173195437797766]
  We aim to train models that mitigate group fairness disparity without causing harm to model accuracy. The current data acquisition methods, such as fair active learning approaches, typically require annotating sensitive attributes. We propose a tractable active data sampling algorithm that does not rely on training group annotations.
  arXiv  Detail & Related papers  (2024-02-20T07:57:38Z)
• Debiasing Vision-Language Models via Biased Prompts [79.04467131711775]
  We propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. We show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models.
  arXiv  Detail & Related papers  (2023-01-31T20:09:33Z)
• Resolving label uncertainty with implicit posterior models [71.62113762278963]
  We propose a method for jointly inferring labels across a collection of data samples. By implicitly assuming the existence of a generative model for which a differentiable predictor is the posterior, we derive a training objective that allows learning under weak beliefs.
  arXiv  Detail & Related papers  (2022-02-28T18:09:44Z)
• Label-Descriptive Patterns and their Application to Characterizing Classification Errors [31.272875287136426]
  State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight into whether a model is prone to making systematic errors, but also gives a way to act and improve the model. In this paper we propose a method that allows us to do so for arbitrary classifiers by mining a small set of patterns that together succinctly describe the input data that is partitioned according to correctness of prediction.
  arXiv  Detail & Related papers  (2021-10-18T19:42:21Z)
• AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses [66.49753193098356]
  We investigate the reason behind the surprising adversarial brittleness of scoring models. Our results indicate that autoscoring models, despite getting trained as "end-to-end" models, behave like bag-of-words models. We propose detection-based protection models that can detect oversensitivity and overstability causing samples with high accuracies.
  arXiv  Detail & Related papers  (2021-09-24T03:49:38Z)
• Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning [6.92027612631023]
  We study the problem of integrating pattern- and fact-based models into one framework. We build a Preference-aware Fake News Detection Framework (Pref-FEND), which learns the respective preferences of pattern- and fact-based models for joint detection.
  arXiv  Detail & Related papers  (2021-09-23T12:28:55Z)
• An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information [64.66785523187845]
  We focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. We propose a new sampling strategy to select additional intra-document image-sentence pairs as positive or negative samples.
  arXiv  Detail & Related papers  (2021-03-21T05:43:29Z)
• TextDecepter: Hard Label Black Box Attack on Text Classifiers [0.0]
  We present a novel approach for hard-label black-box attacks against Natural Language Processing (NLP) classifiers. Such an attack scenario applies to real-world black-box models being used for security-sensitive applications such as sentiment analysis and toxic content detection.
  arXiv  Detail & Related papers  (2020-08-16T08:57:01Z)

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.