mlsanityData to 7 Oct 2026

Class based Influence Functions for Error Detection

arXiv 2305.01384 ↗First posted 2 May 2023cs.CL cs.LGcited by 2

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Abstract (arXiv)

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 2 May 2023.

Builds on

16 of 30 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
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  5. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
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  9. 2020Estimating Training Data Influence by Tracing Gradient Descentcited by 565
  10. 2020Influence Functions in Deep Learning Are Fragilecited by 247
  11. 2018Stronger Data Poisoning Attacks Break Data Sanitization Defensescited by 125
  12. 2021Input Similarity from the Neural Network Perspectivecited by 78
4 more
  1. 2019Detecting Adversarial Samples Using Influence Functions and Nearest Neighborscited by 72
  2. 2018Interpreting Black Box Predictions using Fisher Kernelscited by 48
  3. 2021An Empirical Comparison of Instance Attribution Methods for NLPcited by 35
  4. 2022Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corporacited by 4

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025A Comparative Analysis of Influence Signals for Data Debuggingcited by 0
  2. 2025Detecting and Rectifying Noisy Labels: A Similarity-based Approachcited by 0