Metadata Normalization

• URL: http://arxiv.org/abs/2104.09052v1
• Date: Mon, 19 Apr 2021 05:10:26 GMT
• Title: Metadata Normalization
• Authors: Mandy Lu, Qingyu Zhao, Jiequan Zhang, Kilian M. Pohl, Li Fei-Fei, Juan Carlos Niebles, Ehsan Adeli
• Abstract summary: Batch Normalization (BN) normalizes feature distributions by standardizing with batch statistics. BN does not correct the influence on features from extraneous variables or multiple distributions. We introduce the Metadata Normalization layer, a new batch-level operation which can be used end-to-end within the training framework.
• Score: 54.43363251520749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize feature distributions by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. Such extra variables, referred to as metadata here, may create bias or confounding effects (e.g., race when classifying gender from face images). We introduce the Metadata Normalization (MDN) layer, a new batch-level operation which can be used end-to-end within the training framework, to correct the influence of metadata on feature distributions. MDN adopts a regression analysis technique traditionally used for preprocessing to remove (regress out) the metadata effects on model features during training. We utilize a metric based on distance correlation to quantify the distribution bias from the metadata and demonstrate that our method successfully removes metadata effects on four diverse settings: one synthetic, one 2D image, one video, and one 3D medical image dataset.

Related papers

• Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss [0.4249842620609682]
  We build on the textitinvariant statistical loss (ISL) method introduced in citede2024training. We extend it to handle heavy-tailed and multivariate data distributions. We assess its performance in generative generative modeling and explore its potential as a pretraining technique for generative adversarial networks (GANs)
  arXiv  Detail & Related papers  (2024-10-29T10:27:50Z)
• How Does Data Diversity Shape the Weight Landscape of Neural Networks? [2.89287673224661]
  We investigate the impact of dropout, weight decay, and noise augmentation on the parameter space of neural networks. We observe that diverse data influences the weight landscape in a similar fashion as dropout. We conclude that synthetic data can bring more diversity into real input data, resulting in a better performance on out-of-distribution test instances.
  arXiv  Detail & Related papers  (2024-10-18T16:57:05Z)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Context Normalization Layer with Applications [0.1499944454332829]
  This study proposes a new normalization technique, called context normalization, for image data. It adjusts the scaling of features based on the characteristics of each sample, which improves the model's convergence speed and performance. The effectiveness of context normalization is demonstrated on various datasets, and its performance is compared to other standard normalization techniques.
  arXiv  Detail & Related papers  (2023-03-14T06:38:17Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models [11.818509522227565]
  MetaData Normalization (MDN) estimates the linear relationship between the metadata and each feature based on a non-trainable closed-form solution. We extend the MDN method by applying a Penalty approach (referred to as PDMN) We show improvement in model accuracy and greater independence from confounders using PMDN over MDN in a synthetic experiment and a multi-label, multi-site dataset of magnetic resonance images (MRIs)
  arXiv  Detail & Related papers  (2022-07-11T04:02:12Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)
• Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data [87.61504710345528]
  We propose two strategies for freeing a neural network from tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference.
  arXiv  Detail & Related papers  (2020-02-26T04:18:25Z)

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.