mlsanityData to 14 Oct 2026

Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI

arXiv 2112.03837 ↗First posted 7 Dec 2021cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Data scarcity and noise are important issues in industrial applications of machine learning. However, it is often challenging to devise a scalable and generalized approach to address the fundamental distributional and semantic properties of dataset with black box models. For this reason, data-centric approaches are crucial for the automation of machine learning operation pipeline. In order to serve as the basis for this automation, we suggest a domain-agnostic pipeline for refining the quality of data in image classification problems. This pipeline contains data valuation, cleansing, and augmentation. With an appropriate combination of these methods, we could achieve 84.711% test accuracy (ranked #6, Honorable Mention in the Most Innovative) in the Data-Centric AI competition only with the provided dataset.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 18 references that are in the map. First posted on arXiv 7 Dec 2021.

Builds on

15 of 18 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  5. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  6. 2019A Closer Look at Few-shot Classificationcited by 684
  7. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 665
  8. 2017Data Augmentation Generative Adversarial Networkscited by 278
  9. 2019Fast AutoAugmentcited by 243
  10. 2019Data Valuation using Reinforcement Learningcited by 116
  11. 2019Faster AutoAugment: Learning Augmentation Strategies using Backpropagationcited by 96
  12. 2019Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedbackcited by 79
3 more
  1. 2019Robust Inference via Generative Classifiers for Handling Noisy Labelscited by 68
  2. 2021HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networkscited by 38
  3. 2020Correcting for Selection Bias in Learning-to-rank Systemscited by 35

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2022The Principles of Data-Centric AI (DCAI)cited by 26
  2. 2023Intrinsic Self-Supervision for Data Quality Auditscited by 8