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Found 9 papers, 2 papers with code
Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning
We believe that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications.
CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture
To address these issues, we present the CWD30 dataset, a large-scale, diverse, holistic, and hierarchical dataset tailored for crop-weed recognition tasks in precision agriculture.
Variation-Aware Semantic Image Synthesis
Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years.
TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification
Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is a challenging task due to a variety of issues, such as color inconsistency that results from the non-uniform manual staining operations, clustering of nuclei, and blurry and overlapping nuclei boundaries.
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Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images
This paper presents results of applying Inception v4 deep convolutional neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part a.
Corn leaf detection using Region based convolutional neural network
Based on results of experiments conducted with several state-of-the-art models adopted by CNN, a region-based method has been proposed as a faster and more accurate method of corn leaf detection.
Farm land weed detection with region-based deep convolutional neural networks
Being motivated with such unique attributes of ResNet, this paper evaluates the performance of fine-tuned ResNet for object classification of our weeds dataset.
Genetic Random Weight Change Algorithm for the Learning of Multilayer Neural Networks
A new method to improve the performance of Random weight change (RWC) algorithm based on a simple genetic algorithm, namely, Genetic random weight change (GRWC) is proposed.
Optimizing method for Neural Network based on Genetic Random Weight Change Learning Algorithm
In this paper, we proposed a methodology by combining the RWC and GA, namely Genetic Random Weight Change (GRWC), as well as demonstrate a seminal way to reduce the complexity of the neural network by removing weak weights of GRWC.
