# Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge

arXiv: https://arxiv.org/abs/2009.09289 · HTML: https://mlsanity.com/paper/2009.09289/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://mlsanity.com/paper/2112.06745/) (2021)
- [Deep Least Squares Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.02207/) (2021)
- [House Price Prediction Based On Deep Learning](https://mlsanity.com/paper/2204.09050/) (2022)
- [Confident Pseudo-labeled Diffusion Augmentation for Canine Cardiomegaly Detection](https://mlsanity.com/paper/2501.07533/) (2025)
- [Overview of LifeCLEF Plant Identification task 2020](https://mlsanity.com/paper/2509.19402/) (2025)

## Abstract (arXiv)

Domain adaptation is one of the most crucial techniques to mitigate the domain shift problem, which exists when transferring knowledge from an abundant labeled sourced domain to a target domain with few or no labels. Partial domain adaptation addresses the scenario when target categories are only a subset of source categories. In this paper, to enable the efficient representation of cross-domain plant images, we first extract deep features from pre-trained models and then develop adversarial consistent learning (ACL) in a unified deep architecture for partial domain adaptation. It consists of source domain classification loss, adversarial learning loss, and feature consistency loss. Adversarial learning loss can maintain domain-invariant features between the source and target domains. Moreover, feature consistency loss can preserve the fine-grained feature transition between two domains. We also find the shared categories of two domains via down-weighting the irrelevant categories in the source domain. Experimental results demonstrate that training features from NASNetLarge model with proposed ACL architecture yields promising results on the PlantCLEF 2020 Challenge.