Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah IAES Nawala dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar tentang transfer learning. Transfer learning adalah teknik machine learning yang menggunakan pengetahuan yang diperoleh dari satu task untuk task lain yang terkait. Hal ini mengurangi jumlah pelabelan data dan sumber daya yang diperlukan, serta meningkatkan kinerja dan efisiensi model. Shirahatti dkk. (2023) melakukan penelitian yang bertujuan untuk mendeteksi ironi dan jenis-jenisnya dalam tweet bahasa Inggris menggunakan pendekatan transfer learning. Para penulis mengusulkan sebuah model berdasarkan arsitektur DistilBERT dan jaringan bidirectional long-short term memory (Bi-LSTM) untuk mengklasifikasikan ironi dalam tweet. Sistem yang diusulkan mencapai akurasi 81% untuk non-ironi dan 66% untuk ironi, recall 77% untuk non-ironi dan 72% untuk ironi, dan skor F1 79% untuk non-ironi dan 69% untuk kelas ironi. Penelitian ini memperluas pekerjaan dari klasifikasi biner ke klasifikasi multikelas ironi, memberikan gambaran untuk penelitian selanjutnya mengenai berbagai jenis ironi dalam tweet.
Fine grained irony classification through transfer learning approach
Abhinandan Shirahatti, Vijay Rajpurohit, Sanjeev Sannakki
Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT) light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (Bi-LSTM) network this configuration minimizes data preprocessing tasks proposed model tests on a SemEval-2018 task 3, 3,834 samples were provided. Experiment results show the proposed system has achieved a precision of 81% for not irony class and 66% for irony class, recall of 77% for not irony and 72% for irony, and F1 score of 79% for not irony and 69% for irony class since researchers have come up with a binary classification model, in this study we have extended our work for multiclass classification of irony. It is significant and will serve as a foundation for future research on different types of irony in tweets.
Penelitian selanjutnya dilakukan oleh Rattaphun dan Songsri-in (2023), yang meneliti penggunaan transfer learning untuk mengklasifikasikan gambar budaya Thailand. Hasilnya menunjukkan bahwa ini adalah pendekatan yang efektif, dengan menggunakan tiga model artificial neural network yang telah dilatih sebelumnya.
Thai culture image classification with transfer learning
Munlika Rattaphun, Kritaphat Songsri-in
Classifying images of Thai culture is important for a variety of applications, such as tourism, education, and cultural preservation. However, building a Machine learning model from scratch to classify Thai cultural images can be challenging due to the limited availability of annotated data. In this study, we investigate the use of transfer learning for the task of image classification on a dataset of Thai cultural images. We utilize three popular convolutional neural network models, namely MobileNet, EfficientNet, and residual network (ResNet) as baseline pre-trained models. Their performances were evaluated when they were trained from random initialization, used as a feature extractor, and fully fine-tuned. The results showed that all three models performed better in terms of accuracy and training time when they were used as a feature extractor, with EfficientNet achieving the highest accuracy of 95.87% while maintaining the training time of 24 ms/iteration. To better understand the reasoning behind the predictions made by the models, we deployed the gradient-weighted class activation mapping (Grad-CAM) visualization technique to generate heatmaps that the models attend to when making predictions. Both our quantitative and qualitative experiments demonstrated that transfer learning is an effective approach to image classification on Thai cultural images.
Jain dkk. (2023) mengembangkan kerangka kerja kacamata deteksi secara real-time menggunakan fitur gambar wajah atau mata, khusus pada gambar wajah yang tidak standar. Mereka menggunakan artificial neural network berbasis arsitektur Inception V3 yang memberikan hasil akurasi 99,2% pada pelatihan dan 99,9% pada pengujian.
Real-time eyeglass detection using transfer learning for non-standard facial data
Ritik Jain, Aashi Goyal, Kalaichelvi Venkatesan
The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.
Pada penelitian yang lain, Sadanand dkk. (2023) mencoba mengembangkan sistem pendeteksi pelanggaran aturan masker dan social distancing secara real-time dengan menggunakan pendekatan transfer learning pada model deteksi objek MobileNetV2 dan YOLOv3. Sistem ini memiliki akurasi tinggi dan bisa diintegrasikan dengan kamera IP atau sistem pengawasan.
Social distance and face mask detector system exploiting transfer learning
Vijaya Shetty Sadanand, Keerthi Anand, Pooja Suresh, Punnya Kadyada Arun Kumar, Priyanka Mahabaleshwar
As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.
Pemodelan deteksi penyakit tanaman berbasis artificial neural network dengan transfer learning telah menjadi fokus penelitian. Menurut Adebayo dkk. (2023) penelitian-penelitian tersebut menunjukkan efektivitas dan potensi untuk meningkatkan kinerja model serta mengurangi kebutuhan akan data pelatihan yang banyak.
Convolutional neural network-based crop disease detection model using transfer learning approach
Segun Adebayo, Halleluyah Oluwatobi Aworinde, Akinwale O. Akinwunmi, Adebamiji Ayandiji, Awoniran Olalekan Monsir
Crop diseases disrupt the crop’s physiological constitution by affecting the crop’s natural state. The physical recognition of the symptoms of the various diseases has largely been used to diagnose cassava infections. Every disease has a distinct set of symptoms that can be used to identify it. Early detection through physical identification, however, is quite difficult for a vast crop field. The use of electronic tools for illness identification then becomes necessary to promote early disease detection and control. Convolutional neural networks (CNN) were investigated in this study for the electronic identification and categorization of photographs of cassava leaves. For feature extraction and classification, the study used databases of cassava images and a deep convolutional neural network model. The methodology of this study retrained the models’ current weights for visual geometry group (VGG-16), VGG-19, SqueezeNet, and MobileNet. Accuracy, loss, model complexity, and training time were all taken into consideration when evaluating how well the final layer of CNN models performed when trained on the new cassava image datasets.
Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai transfer learning. Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman dan membaca artikel secara GRATIS melalui tautan-tautan berikut: http://iaesprime.com/index.php/csit/, https://ijece.iaescore.com/, dan https://ijeecs.iaescore.com/.
Redaksi: I. Busthomi.