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This study delves into the challenge of classifying visually similar bird species, an area of significant interest in the field of fine-grained image classification. Utilizing a substantial dataset comprising images of ten bird species which was selected carefully to challenge the model to classify species of extreme similarities. To achieve this, we were keen to collect the data with subtle visual dissimilarities and of different positions taken for these birds. The research explores the potential of deep learning techniques to differentiate species based on subtle inter-species variations. This task is particularly demanding due to the minimal yet critical differences between these closely related species. Our research leveraged a unique deep learning model using convolutional neural networks (CNNs) to accurately classify birds with minimal visual differences. This innovative approach marks a significant step forward in machine learning for biological classification, with implications for biodiversity and ecological conservation. Our study demonstrates the effectiveness of our deep learning model in accurately classifying bird species, showcasing the potential of advanced techniques in complex Classification tasks. This research enhances the use of computational methods in biodiversity and ecological conservation. Additionally, it underscores the importance of birds as indicators of environmental changes, such as climate shifts, aiding in early detection of potential ecological issues. [ABSTRACT FROM AUTHOR]

This work addresses the challenge of classifying visually similar bird species, a task complicated by subtle interspecies variations. We focused on ten bird species, assembling a dataset of approximately 8000 images from Google Images. These species were specifically chosen for their high degree of similarity, presenting a unique challenge for classification algorithms. To enhance our dataset and improve classification accuracy, we employed Generative adversarial networks (GANs), a state-of-the-art generative adversarial network, to augment our original dataset with synthetic yet realistic images. This augmentation aimed to provide a more prosperous, diverse training environment for our deep learning model. Subsequently, we developed a specialized multi-classification model tailored to recognize and differentiate these closely related bird species. Integrating GANs like StyleGAN3-augmented data into our training process represents a novel approach to ecological image analysis, potentially setting a new standard for accuracy and efficiency in classifying highly similar species. This study demonstrates the effectiveness of advanced generative models in complex classification tasks and contributes a valuable methodology to ecological research and species identification. [ABSTRACT FROM AUTHOR]

The correction of grammatical errors in natural language processing is a crucial task as it aims to enhance the accuracy and intelligibility of written language. However, developing a grammatical error correction (GEC) framework for low-resource languages presents significant challenges due to the lack of available training data. This article proposes a novel GEC framework for low-resource languages, using Arabic as a case study. To generate more training data, we propose a semi-supervised confusion method called the equal distribution of synthetic errors (EDSE), which generates a wide range of parallel training data. Additionally, this article addresses two limitations of the classical seq2seq GEC model, which are unbalanced outputs due to the unidirectional decoder and exposure bias during inference. To overcome these limitations, we apply a knowledge distillation technique from neural machine translation. This method utilizes two decoders, a forward decoder right-to-left and a backward decoder left-to-right, and measures their agreement using Kullback-Leibler divergence as a regularization term. The experimental results on two benchmarks demonstrate that our proposed framework outperforms the Transformer baseline and two widely used bidirectional decoding techniques, namely asynchronous and synchronous bidirectional decoding. Furthermore, the proposed framework reported the highest F1 score, and generating synthetic data using the equal distribution technique for syntactic errors resulted in a significant improvement in performance. These findings demonstrate the effectiveness of the proposed framework for improving grammatical error correction for low-resource languages, particularly for the Arabic language. [ABSTRACT FROM AUTHOR]

Grammatical Error Correction (GEC) refers to the automatic identification and amendment of grammatical, spelling, punctuation, and word-positioning errors in monolingual texts. Neural Machine Translation (NMT) is nowadays one of the most valuable techniques used for GEC but it may suffer from scarcity of training data and domain shift, depending on the addressed language. However, current techniques (e.g., tuning pre-trained language models or developing spell-confusion methods without focusing on language diversity) tackling the data sparsity problem associated with NMT create mismatched data distributions. This paper proposes new aggressive transformation approaches to augment data during training that extend the distribution of authentic data. In particular, it uses augmented data as auxiliary tasks to provide new contexts when the target prefix is not helpful for the next word prediction. This enhances the encoder and steadily increases its contribution by forcing the GEC model to pay more attention to the text representations of the encoder during decoding. The impact of these approaches was investigated using the Transformer-based for low-resource GEC task, and Arabic GEC was used as a case study. GEC models trained with our data tend more to source information, are more domain shift robustness, and have less hallucinations with tiny training datasets and domain shift. Experimental results showed that the proposed approaches outperformed the baseline, the most common data augmentation methods, and classical synthetic data approaches. In addition, a combination of the three best approaches Misspelling , Swap , and Reverse achieved the best F 1 score in two benchmarks and outperformed previous Arabic GEC approaches. [ABSTRACT FROM AUTHOR]