W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality

• URL: http://arxiv.org/abs/2103.13107v1
• Date: Wed, 24 Mar 2021 11:28:59 GMT
• Title: W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality
• Authors: Francesco Ponzio, Enrico Macii, Elisa Ficarra, Santa Di Cataldo
• Abstract summary: Wise2WipedNet (W2WNet) is a new two- module Convolutional Neural Network. A Wise module exploits Bayesian inference to identify and discard spurious images during the training. A Wiped module takes care of the final classification while broadcasting information on the prediction confidence at inference time.
• Score: 2.695466667982714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional Neural Networks (CNNs) are supposed to be fed with only high-quality annotated datasets. Nonetheless, in many real-world scenarios, such high quality is very hard to obtain, and datasets may be affected by any sort of image degradation and mislabelling issues. This negatively impacts the performance of standard CNNs, both during the training and the inference phase. To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training, and a Wiped module takes care of the final classification while broadcasting information on the prediction confidence at inference time. The goodness of our solution is demonstrated on a number of public benchmarks addressing different image classification tasks, as well as on a real-world case study on histological image analysis. Overall, our experiments demonstrate that W2WNet is able to identify image degradation and mislabelling issues both at training and at inference time, with a positive impact on the final classification accuracy.

Related papers

• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Decoupled Mixup for Generalized Visual Recognition [71.13734761715472]
  We propose a novel "Decoupled-Mixup" method to train CNN models for visual recognition. Our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions to train CNN models. Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts.
  arXiv  Detail & Related papers  (2022-10-26T15:21:39Z)
• CONVIQT: Contrastive Video Quality Estimator [63.749184706461826]
  Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.
  arXiv  Detail & Related papers  (2022-06-29T15:22:01Z)
• Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network [20.835800149919145]
  Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality. There is a performance drop when assessing distortion images generated by generative adversarial network (GAN) with seemingly realistic texture. We propose an Attention-based Hybrid Image Quality Assessment Network (AHIQ) to deal with the challenge and get better performance on the GAN-based IQA task.
  arXiv  Detail & Related papers  (2022-04-22T03:59:18Z)
• Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment [73.61888777504377]
  Full-reference (FR) image quality assessment (IQA) evaluates the visual quality of a distorted image by measuring its perceptual difference with pristine-quality reference. Unlabeled data can be easily collected from an image degradation or restoration process, making it encouraging to exploit unlabeled training data to boost FR-IQA performance. In this paper, we suggest to incorporate semi-supervised and positive-unlabeled (PU) learning for exploiting unlabeled data while mitigating the adverse effect of outliers.
  arXiv  Detail & Related papers  (2022-04-19T09:10:06Z)
• Synergistic Network Learning and Label Correction for Noise-robust Image Classification [28.27739181560233]
  Deep Neural Networks (DNNs) tend to overfit training label noise, resulting in poorer model performance in practice. We propose a robust label correction framework combining the ideas of small loss selection and noise correction. We demonstrate our method on both synthetic and real-world datasets with different noise types and rates.
  arXiv  Detail & Related papers  (2022-02-27T23:06:31Z)
• Efficient and Robust Classification for Sparse Attacks [34.48667992227529]
  We consider perturbations bounded by the $ell$--norm, which have been shown as effective attacks in the domains of image-recognition, natural language processing, and malware-detection. We propose a novel defense method that consists of "truncation" and "adrial training" Motivated by the insights we obtain, we extend these components to neural network classifiers.
  arXiv  Detail & Related papers  (2022-01-23T21:18:17Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)
• Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning [0.0]
  ImageNet was the backbone of various convolutional neural networks (CNNs) trained on ILSVRC12Net. This paper describes automated applications based on model consensus, explainability and confident learning to correct labeling mistakes. The ImageNet-Clean improves the model performance by 2-2.4 % for SqueezeNet and EfficientNet-B0 models.
  arXiv  Detail & Related papers  (2021-03-30T13:16:35Z)
• Pairwise Relation Learning for Semi-supervised Gland Segmentation [90.45303394358493]
  We propose a pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net) We evaluate our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset.
  arXiv  Detail & Related papers  (2020-08-06T15:02:38Z)

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.