# Regularization Through Simultaneous Learning: A Case Study on Plant Classification

arXiv: https://arxiv.org/abs/2305.13447 · HTML: https://mlsanity.com/paper/2305.13447/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 74 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Fast is better than free: Revisiting adversarial training](https://mlsanity.com/paper/2001.03994/) (2020)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [L2 Regularization versus Batch and Weight Normalization](https://mlsanity.com/paper/1706.05350/) (2017)
- [Auxiliary Tasks in Multi-task Learning](https://mlsanity.com/paper/1805.06334/) (2018)
- [Regularization for Deep Learning: A Taxonomy](https://mlsanity.com/paper/1710.10686/) (2017)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [Curriculum Dropout](https://mlsanity.com/paper/1703.06229/) (2017)
- [A Modulation Module for Multi-task Learning with Applications in Image Retrieval](https://mlsanity.com/paper/1807.06708/) (2018)
- [Environment-agnostic Multitask Learning for Natural Language Grounded Navigation](https://mlsanity.com/paper/2003.00443/) (2020)
- [Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift](https://mlsanity.com/paper/1801.05134/) (2018)
- [AutoDropout: Learning Dropout Patterns to Regularize Deep Networks](https://mlsanity.com/paper/2101.01761/) (2021)
- [An empirical analysis of dropout in piecewise linear networks](https://mlsanity.com/paper/1312.6197/) (2013)
- [AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving](https://mlsanity.com/paper/1901.05808/) (2019)
- [Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems](https://mlsanity.com/paper/2110.04960/) (2021)
- [LocalDrop: A Hybrid Regularization for Deep Neural Networks](https://mlsanity.com/paper/2103.00719/) (2021)

## Abstract (arXiv)

In response to the prevalent challenge of overfitting in deep neural networks, this paper introduces Simultaneous Learning, a regularization approach drawing on principles of Transfer Learning and Multi-task Learning. We leverage auxiliary datasets with the target dataset, the UFOP-HVD, to facilitate simultaneous classification guided by a customized loss function featuring an inter-group penalty. This experimental configuration allows for a detailed examination of model performance across similar (PlantNet) and dissimilar (ImageNet) domains, thereby enriching the generalizability of Convolutional Neural Network models. Remarkably, our approach demonstrates superior performance over models without regularization and those applying dropout regularization exclusively, enhancing accuracy by 5 to 22 percentage points. Moreover, when combined with dropout, the proposed approach improves generalization, securing state-of-the-art results for the UFOP-HVD challenge. The method also showcases efficiency with significantly smaller sample sizes, suggesting its broad applicability across a spectrum of related tasks. In addition, an interpretability approach is deployed to evaluate feature quality by analyzing class feature correlations within the network's convolutional layers. The findings of this study provide deeper insights into the efficacy of Simultaneous Learning, particularly concerning its interaction with the auxiliary and target datasets.