An exploratory journey of representation learning’s enhancement, adaptation and related intelligent methods
Year: 2024
Type: article
Abstract: Representation learning models employing Siamese structures have consistently demonstrated exceptional performance across various fields, including deep learning, computer vision, and natural language processing.Furthermore, the applicability of representation learning has broadened to encompass wider domains such as agriculture, remote sensing, and earth observation, which are significantly challenged by data scarcity.This dissertation aims to enhance the quality and adaptability of learned representations across these diverse application domains.Meanwhile, we have also expanded the scope of our research to a broader area of intelligent agricultural systems.Initially, we delve into contrastive representation learning within the general computer vision domain and introduce a novel "Hallucinator" module to reduce mutual information, increase the batch size of positive pairs, and improve representation quality.Subsequently, we extend the representation framework to agriculture and remote sensing, proposing spatial-temporal-aware architectures tailored to the unique characteristics of remote sensing data.Furthermore, we introduce the Extended Agriculture Vision dataset to address data scarcity issues and showcase the effectiveness of proposed representation frameworks.Furthermore, we demonstrate that the learned representations are powerful features for few-shot tasks in remote sensing and earth observation.We introduce GenCo, a generator-based representation learning
Author: Wu, Jing
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220815992