Image Classification using Sequence of Pixels

• URL: http://arxiv.org/abs/2209.11495v1
• Date: Fri, 23 Sep 2022 09:42:44 GMT
• Title: Image Classification using Sequence of Pixels
• Authors: Gajraj Kuldeep
• Abstract summary: This study compares sequential image classification methods based on recurrent neural networks. We describe methods based on Long-Short-Term memory(LSTM), bidirectional Long-Short-Term memory(BiLSTM) architectures, etc.
• Score: 3.04585143845864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study compares sequential image classification methods based on recurrent neural networks. We describe methods based on recurrent neural networks such as Long-Short-Term memory(LSTM), bidirectional Long-Short-Term memory(BiLSTM) architectures, etc. We also review the state-of-the-art sequential image classification architectures. We mainly focus on LSTM, BiLSTM, temporal convolution network, and independent recurrent neural network architecture in the study. It is known that RNN lacks in learning long-term dependencies in the input sequence. We use a simple feature construction method using orthogonal Ramanujan periodic transform on the input sequence. Experiments demonstrate that if these features are given to LSTM or BiLSTM networks, the performance increases drastically. Our focus in this study is to increase the training accuracy simultaneously reducing the training time for the LSTM and BiLSTM architecture, but not on pushing the state-of-the-art results, so we use simple LSTM/BiLSTM architecture. We compare sequential input with the constructed feature as input to single layer LSTM and BiLSTM network for MNIST and CIFAR datasets. We observe that sequential input to the LSTM network with 128 hidden unit training for five epochs results in training accuracy of 33% whereas constructed features as input to the same LSTM network results in training accuracy of 90% with 1/3 lesser time.

Related papers

• Unlocking the Power of LSTM for Long Term Time Series Forecasting [27.245021350821638]
  We propose a simple yet efficient algorithm named P-sLSTM built upon sLSTM by incorporating patching and channel independence. These modifications substantially enhance sLSTM's performance in TSF, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-08-19T13:59:26Z)
• RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning [75.61681328968714]
  We propose recurrent independent Grid LSTM (RigLSTM) to exploit the underlying modular structure of the target task. Our model adopts cell selection, input feature selection, hidden state selection, and soft state updating to achieve a better generalization ability.
  arXiv  Detail & Related papers  (2023-11-03T07:40:06Z)
• Towards Energy-Efficient, Low-Latency and Accurate Spiking LSTMs [1.7969777786551424]
  Spiking Neural Networks (SNNs) have emerged as an attractive-temporal computing paradigm vision for complex tasks. We propose an optimized spiking long short-term memory networks (LSTM) training framework that involves a novel. rev-to-SNN conversion framework, followed by SNN training. We evaluate our framework on sequential learning tasks including temporal M, Google Speech Commands (GSC) datasets, and UCI Smartphone on different LSTM architectures.
  arXiv  Detail & Related papers  (2022-10-23T04:10:27Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• A journey in ESN and LSTM visualisations on a language task [77.34726150561087]
  We trained ESNs and LSTMs on a Cross-Situationnal Learning (CSL) task. The results are of three kinds: performance comparison, internal dynamics analyses and visualization of latent space.
  arXiv  Detail & Related papers  (2020-12-03T08:32:01Z)
• Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone [5.927250637620123]
  We propose a new LSTM variant called embedded convolutional LSTM (E NeuralTM) In ETM a group of different 1D convolutions is embedded into the LSTM structure. Through this, the temporal information is preserved between and within windows. We show the superiority of our proposed ETM approach over the state-of-the-art approaches on several widely used benchmark data sets for RUL Estimation.
  arXiv  Detail & Related papers  (2020-08-10T08:34:20Z)
• Object Tracking through Residual and Dense LSTMs [67.98948222599849]
  Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative. DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
  arXiv  Detail & Related papers  (2020-06-22T08:20:17Z)
• Achieving Online Regression Performance of LSTMs with Simple RNNs [0.0]
  We introduce a first-order training algorithm with a linear time complexity in the number of parameters. We show that when SRNNs are trained with our algorithm, they provide very similar regression performance with the LSTMs in two to three times shorter training time.
  arXiv  Detail & Related papers  (2020-05-16T11:41:13Z)
• Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks [1.5146765382501612]
  We perform sentiment analysis on a GOP Debate Twitter dataset. To speed up training and reduce the computational cost and time, six different parameter reduced slim versions of the LSTM model are proposed.
  arXiv  Detail & Related papers  (2020-05-08T12:50:10Z)
• Depth-Adaptive Graph Recurrent Network for Text Classification [71.20237659479703]
  Sentence-State LSTM (S-LSTM) is a powerful and high efficient graph recurrent network. We propose a depth-adaptive mechanism for the S-LSTM, which allows the model to learn how many computational steps to conduct for different words as required.
  arXiv  Detail & Related papers  (2020-02-29T03:09:55Z)

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.