Merged-GHCIDR: Geometrical Approach to Reduce Image Data

• URL: http://arxiv.org/abs/2209.02609v1
• Date: Tue, 6 Sep 2022 16:03:15 GMT
• Title: Merged-GHCIDR: Geometrical Approach to Reduce Image Data
• Authors: Devvrat Joshi, Janvi Thakkar, Siddharth Soni, Shril Mody, Rohan Patil, Nipun Batra
• Abstract summary: Training neural networks on massive datasets have become a challenging and time-consuming task. We present novel variations of an earlier approach called reduction through homogeneous clustering for reducing dataset size. We propose two variations: Geometrical Homogeneous Clustering for Image Data Reduction (GHCIDR) and Merged-GHCIDR upon the baseline algorithm.
• Score: 2.290085549352983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The computational resources required to train a model have been increasing since the inception of deep networks. Training neural networks on massive datasets have become a challenging and time-consuming task. So, there arises a need to reduce the dataset without compromising the accuracy. In this paper, we present novel variations of an earlier approach called reduction through homogeneous clustering for reducing dataset size. The proposed methods are based on the idea of partitioning the dataset into homogeneous clusters and selecting images that contribute significantly to the accuracy. We propose two variations: Geometrical Homogeneous Clustering for Image Data Reduction (GHCIDR) and Merged-GHCIDR upon the baseline algorithm - Reduction through Homogeneous Clustering (RHC) to achieve better accuracy and training time. The intuition behind GHCIDR involves selecting data points by cluster weights and geometrical distribution of the training set. Merged-GHCIDR involves merging clusters having the same labels using complete linkage clustering. We used three deep learning models- Fully Connected Networks (FCN), VGG1, and VGG16. We experimented with the two variants on four datasets- MNIST, CIFAR10, Fashion-MNIST, and Tiny-Imagenet. Merged-GHCIDR with the same percentage reduction as RHC showed an increase of 2.8%, 8.9%, 7.6% and 3.5% accuracy on MNIST, Fashion-MNIST, CIFAR10, and Tiny-Imagenet, respectively.

Related papers

• DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers [34.282971510732736]
  We introduce DiTMoS, a novel DNN training and inference framework with a selector-classifiers architecture. A composition of weak models can exhibit high diversity and the union of them can significantly boost the accuracy upper bound. We deploy DiTMoS on the Neucleo STM32F767ZI board and evaluate it based on three time-series datasets for human activity recognition, keywords spotting, and emotion recognition.
  arXiv  Detail & Related papers  (2024-03-14T02:11:38Z)
• Dink-Net: Neural Clustering on Large Graphs [59.10189693120368]
  A deep graph clustering method (Dink-Net) is proposed with the idea of dilation and shrink. By discriminating nodes, whether being corrupted by augmentations, representations are learned in a self-supervised manner. The clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss. Compared to the runner-up, Dink-Net 9.62% achieves NMI improvement on the ogbn-papers100M dataset with 111 million nodes and 1.6 billion edges.
  arXiv  Detail & Related papers  (2023-05-28T15:33:24Z)
• EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
  We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. Our proposed methods consistently outperform several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-19T09:08:43Z)
• Prompt Tuning for Parameter-efficient Medical Image Segmentation [79.09285179181225]
  We propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. We pre-train this architecture with a dedicated dense self-supervision scheme based on assignments to online generated prototypes. We demonstrate that the resulting neural network model is able to attenuate the gap between fully fine-tuned and parameter-efficiently adapted models.
  arXiv  Detail & Related papers  (2022-11-16T21:55:05Z)
• Geometrical Homogeneous Clustering for Image Data Reduction [2.290085549352983]
  We present novel variations of an earlier approach called homogeneous clustering algorithm for reducing dataset size. We experimented with the four variants on three datasets- MNIST, CIFAR10, and Fashion-MNIST. We found that GHCIDR gave the best accuracy of 99.35%, 81.10%, and 91.66% and a training data reduction of 87.27%, 32.34%, and 76.80% respectively.
  arXiv  Detail & Related papers  (2022-08-27T19:42:46Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Feature transforms for image data augmentation [74.12025519234153]
  In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we build ensembles on the data level by adding images generated by combining fourteen augmentation approaches. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method.
  arXiv  Detail & Related papers  (2022-01-24T14:12:29Z)
• Learning Hierarchical Graph Neural Networks for Image Clustering [81.5841862489509]
  We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
  arXiv  Detail & Related papers  (2021-07-03T01:28:42Z)
• Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks [24.017988997693262]
  We study the problem of clustering nodes in a dynamic graph, where the connections between nodes and nodes' cluster memberships may change over time. We first propose a simple decay-based clustering algorithm that clusters nodes based on weighted connections between them, where the weight decreases at a fixed rate over time. We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.
  arXiv  Detail & Related papers  (2020-12-16T04:31:19Z)

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.