Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification

• URL: http://arxiv.org/abs/2307.14825v1
• Date: Thu, 27 Jul 2023 13:01:49 GMT
• Title: Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification
• Authors: Dimitri Korsch, Maha Shadaydeh, Joachim Denzler
• Abstract summary: Fine-grained classification models are often deployed to determine animal species or individuals in automated animal monitoring systems. Attention- or gradient-based methods are commonly used to identify regions in the image that contribute the most to the classification decision. This paper presents a solution to circumvent these computational instabilities by simplifying the CD sampling and reducing reliance on large mini-batch sizes.
• Score: 8.330791157878137
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fine-grained classification is a particular case of a classification problem, aiming to classify objects that share the visual appearance and can only be distinguished by subtle differences. Fine-grained classification models are often deployed to determine animal species or individuals in automated animal monitoring systems. Precise visual explanations of the model's decision are crucial to analyze systematic errors. Attention- or gradient-based methods are commonly used to identify regions in the image that contribute the most to the classification decision. These methods deliver either too coarse or too noisy explanations, unsuitable for identifying subtle visual differences reliably. However, perturbation-based methods can precisely identify pixels causally responsible for the classification result. Fill-in of the dropout (FIDO) algorithm is one of those methods. It utilizes the concrete dropout (CD) to sample a set of attribution masks and updates the sampling parameters based on the output of the classification model. A known problem of the algorithm is a high variance in the gradient estimates, which the authors have mitigated until now by mini-batch updates of the sampling parameters. This paper presents a solution to circumvent these computational instabilities by simplifying the CD sampling and reducing reliance on large mini-batch sizes. First, it allows estimating the parameters with smaller mini-batch sizes without losing the quality of the estimates but with a reduced computational effort. Furthermore, our solution produces finer and more coherent attribution masks. Finally, we use the resulting attribution masks to improve the classification performance of a trained model without additional fine-tuning of the model.

Related papers

• Accurate Explanation Model for Image Classifiers using Class Association Embedding [5.378105759529487]
  We propose a generative explanation model that combines the advantages of global and local knowledge. Class association embedding (CAE) encodes each sample into a pair of separated class-associated and individual codes. Building-block coherency feature extraction algorithm is proposed that efficiently separates class-associated features from individual ones.
  arXiv  Detail & Related papers  (2024-06-12T07:41:00Z)
• MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation [31.648523213206595]
  Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. We propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask.
  arXiv  Detail & Related papers  (2023-03-09T08:24:02Z)
• 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)
• Minimax Active Learning [61.729667575374606]
  Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either rely on model uncertainty to select the most uncertain samples or use clustering or reconstruction to choose the most diverse set of unlabeled examples. We develop a semi-supervised minimax entropy-based active learning algorithm that leverages both uncertainty and diversity in an adversarial manner.
  arXiv  Detail & Related papers  (2020-12-18T19:03:40Z)
• Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples [18.76745359031975]
  Inverse classification is a post modeling process to find changes in input features of samples to alter the initially predicted class. In this study, we propose a new framework to solve inverse classification that maximizes the number of perturbed samples. We design algorithms to solve this problem based on gradient methods, processes, Lagrangian relaxations, and the Gumbel trick.
  arXiv  Detail & Related papers  (2020-09-29T15:52:10Z)
• HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification [14.005379068469361]
  Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. We propose a semi-supervised deep multi-task classification and localization approach HydraMix-Net in the field of medical imagining.
  arXiv  Detail & Related papers  (2020-08-11T15:00:59Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)
• Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches [52.67723703088284]
  We propose a novel framework called multi-patch generative adversarial nets (MPGAN) MPGAN synthesises local patch features and labels unseen classes with a novel weighted voting strategy. MPGAN has significantly greater accuracy than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-27T05:49:44Z)
• Bayesian Few-Shot Classification with One-vs-Each P\'olya-Gamma Augmented Gaussian Processes [7.6146285961466]
  Few-shot classification (FSC) is an important step on the path toward human-like machine learning. We propose a novel combination of P'olya-Gamma augmentation and the one-vs-each softmax approximation that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks.
  arXiv  Detail & Related papers  (2020-07-20T19:10:41Z)
• Few-Shot Open-Set Recognition using Meta-Learning [72.15940446408824]
  The problem of open-set recognition is considered. A new oPen sEt mEta LEaRning (PEELER) algorithm is introduced.
  arXiv  Detail & Related papers  (2020-05-27T23:49:26Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

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.