# Offline and Online Deep Learning for Image Recognition

## TL;DR

This paper explores improvements in image recognition using deep learning, focusing on offline and online classifiers with CNN and MLP variations, providing preliminary but promising results for future research.

## Contribution

It investigates both offline and online deep learning approaches for image classification, highlighting the potential of CNN and MLP variations in these settings.

## Key findings

- Preliminary results show promising accuracy improvements.
- Insights into offline and online classifier performance.
- Directions for future research in deep learning image recognition.

## Abstract

Image recognition using Deep Learning has been evolved for decades though advances in the field through different settings is still a challenge. In this paper, we present our findings in searching for better image classifiers in offline and online environments. We resort to Convolutional Neural Network and its variations of fully connected Multi-layer Perceptron. Though still preliminary, these results are encouraging and may provide a better understanding about the field and directions toward future works.