Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data

• URL: http://arxiv.org/abs/2007.08361v1
• Date: Thu, 16 Jul 2020 14:36:04 GMT
• Title: Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data
• Authors: Ryan Cohn (1) and Elizabeth Holm (1) ((1) Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, USA)
• Abstract summary: This paper demonstrates how to construct, use, and evaluate a high performance unsupervised machine learning system for classifying images. We use the VGG16 convolutional neural network pre-trained on the ImageNet dataset of natural images to extract feature representations for each micrograph. The approach achieves $99.4% pm 0.16%$ accuracy, and the resulting model can be used to classify new images without retraining.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high performance unsupervised machine learning system for classifying images in a popular microstructural dataset. The Northeastern University Steel Surface Defects Database includes micrographs of six different defects observed on hot-rolled steel in a format that is convenient for training and evaluating models for image classification. We use the VGG16 convolutional neural network pre-trained on the ImageNet dataset of natural images to extract feature representations for each micrograph. After applying principal component analysis to extract signal from the feature descriptors, we use k-means clustering to classify the images without needing labeled training data. The approach achieves $99.4\% \pm 0.16\%$ accuracy, and the resulting model can be used to classify new images without retraining This approach demonstrates an improvement in both performance and utility compared to a previous study. A sensitivity analysis is conducted to better understand the influence of each step on the classification performance. The results provide insight toward applying unsupervised machine learning techniques to problems of interest in materials science.

Related papers

• Mitigating Bias Using Model-Agnostic Data Attribution [2.9868610316099335]
  Mitigating bias in machine learning models is a critical endeavor for ensuring fairness and equity. We propose a novel approach to address bias by leveraging pixel image attributions to identify and regularize regions of images containing bias attributes.
  arXiv  Detail & Related papers  (2024-05-08T13:00:56Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing [76.72662577101988]
  This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.
  arXiv  Detail & Related papers  (2023-07-14T14:36:58Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• Prefix Conditioning Unifies Language and Label Supervision [84.11127588805138]
  We show that dataset biases negatively affect pre-training by reducing the generalizability of learned representations. In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.
  arXiv  Detail & Related papers  (2022-06-02T16:12:26Z)
• Graph-based Active Learning for Semi-supervised Classification of SAR Data [8.92985438874948]
  We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods. CNNVAE feature embedding and graph construction requires no labeled data, which reduces overfitting. The method easily incorporates a human-in-the-loop for active learning in the data-labeling process.
  arXiv  Detail & Related papers  (2022-03-31T00:14:06Z)
• Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis [48.02011627390706]
  We develop a novel self-supervised approach that occludes targeted regions to improve the pre-training procedure. We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
  arXiv  Detail & Related papers  (2022-03-25T19:05:06Z)
• Digital Fingerprinting of Microstructures [44.139970905896504]
  Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. Here, we consider microstructure classification and utilise the resulting features over a range of related machine learning tasks. In particular, methods that leverage transfer learning with convolutional neural networks (CNNs), pretrained on the ImageNet dataset, are generally shown to outperform other methods.
  arXiv  Detail & Related papers  (2022-03-25T15:40:44Z)
• Exploiting the relationship between visual and textual features in social networks for image classification with zero-shot deep learning [0.0]
  In this work, we propose a classifier ensemble based on the transferable learning capabilities of the CLIP neural network architecture. Our experiments, based on image classification tasks according to the labels of the Places dataset, are performed by first considering only the visual part. Considering the associated texts to the images can help to improve the accuracy depending on the goal.
  arXiv  Detail & Related papers  (2021-07-08T10:54:59Z)
• From ImageNet to Image Classification: Contextualizing Progress on Benchmarks [99.19183528305598]
  We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset. Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for.
  arXiv  Detail & Related papers  (2020-05-22T17:39:16Z)
• Extracting dispersion curves from ambient noise correlations using deep learning [1.0237120900821557]
  We present a machine-learning approach to classifying the phases of surface wave dispersion curves. Standard FTAN analysis of surfaces observed on an array of receivers is converted to an image. We use a convolutional neural network (U-net) architecture with a supervised learning objective and incorporate transfer learning.
  arXiv  Detail & Related papers  (2020-02-05T23:41:12Z)

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.