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Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches
Using deep learning techniques, we have developed a methodology which can detect different levels of abnormality (i. e., low, medium, high or no abnormality) in maize plants independently of their growth stage.
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems
We merge the capabilities of both supervised and unsupervised approaches in our method to generate new adversarial samples which aid in improving model robustness.
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder
Class imbalance is a pervasive issue in the field of disease classification from medical images.
Adaptive Weight Assignment Scheme For Multi-task Learning
To train a model in multi-task learning settings we need to sum the loss values from different tasks.
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation
Furthermore, We have also introduced the concept of Inverse FGSM (InvFGSM), which works in the opposite manner of FGSM for the data augmentation.
Adversarial Attacks and Defense on Texts: A Survey
Deep learning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis, and many others.
