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[HTML][HTML] Data augmentation: A comprehensive survey of modern approaches
A Mumuni, F Mumuni - Array, 2022 - Elsevier
To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
[HTML][HTML] A comprehensive survey of image augmentation techniques for deep learning
Although deep learning has achieved satisfactory performance in computer vision, a large
volume of images is required. However, collecting images is often expensive and …
volume of images is required. However, collecting images is often expensive and …
Smoothllm: Defending large language models against jailbreaking attacks
Despite efforts to align large language models (LLMs) with human intentions, widely-used
LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an …
LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an …
Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection
Recent studies in deepfake detection have yielded promising results when the training and
testing face forgeries are from the same dataset. However, the problem remains challenging …
testing face forgeries are from the same dataset. However, the problem remains challenging …
[HTML][HTML] Automated data processing and feature engineering for deep learning and big data applications: a survey
A Mumuni, F Mumuni - Journal of Information and Intelligence, 2024 - Elsevier
Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly
from data. This approach has achieved impressive results and has contributed significantly …
from data. This approach has achieved impressive results and has contributed significantly …
A systematic review on data scarcity problem in deep learning: solution and applications
Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …
applications. Deep learning models require a large amount of data to train the model. In …
Las-at: adversarial training with learnable attack strategy
Adversarial training (AT) is always formulated as a minimax problem, of which the
performance depends on the inner optimization that involves the generation of adversarial …
performance depends on the inner optimization that involves the generation of adversarial …
Semi-supervised and unsupervised deep visual learning: A survey
State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …
training data. However, requiring exhaustive manual annotations may degrade the model's …
Trivialaugment: Tuning-free yet state-of-the-art data augmentation
Automatic augmentation methods have recently become a crucial pillar for strong model
performance in vision tasks. While existing automatic augmentation methods need to trade …
performance in vision tasks. While existing automatic augmentation methods need to trade …
A comprehensive survey of neural architecture search: Challenges and solutions
Deep learning has made substantial breakthroughs in many fields due to its powerful
automatic representation capabilities. It has been proven that neural architecture design is …
automatic representation capabilities. It has been proven that neural architecture design is …