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Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification
As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used.
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation
Through our experiments, we determine that on a benchmark dataset, the robustness can be the price of the classification accuracy with performance reductions of 3%-20% for regular tests and gains of 50%-70% for adversarial attack tests.
RoBERTweet: A BERT Language Model for Romanian Tweets
Developing natural language processing (NLP) systems for social media analysis remains an important topic in artificial intelligence research.
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