IRJS – Deep Learning
*Indexed in Scopus and Emerging Sources Citation Index (ESCI) - Web of Science*
NEWS AND EVENTS
New issue: Advances in Transformer Architectures
Call for papers: Robustness and Generalization in Deep Learning
Special collection: Explainable AI for Deep Models
Workshop: Deep Learning for Medical Imaging Applications
Tutorial: Generative Models and Diffusion Techniques
Dataset release: Large-Scale Vision-Language Benchmark
Tool release: Pretrained Vision Transformer Toolkit
Survey published: Self-Supervised Learning Methods
New issue: Advances in Transformer Architectures
Call for papers: Robustness and Generalization in Deep Learning
Special collection: Explainable AI for Deep Models
Workshop: Deep Learning for Medical Imaging Applications
Tutorial: Generative Models and Diffusion Techniques
Dataset release: Large-Scale Vision-Language Benchmark
Tool release: Pretrained Vision Transformer Toolkit
Survey published: Self-Supervised Learning Methods
View all News
NEWS AND EVENTS
New issue: Advances in Transformer Architectures
Call for papers: Robustness and Generalization in Deep Learning
Special collection: Explainable AI for Deep Models
Workshop: Deep Learning for Medical Imaging Applications
Tutorial: Generative Models and Diffusion Techniques
Dataset release: Large-Scale Vision-Language Benchmark
Tool release: Pretrained Vision Transformer Toolkit
Survey published: Self-Supervised Learning Methods
New issue: Advances in Transformer Architectures
Call for papers: Robustness and Generalization in Deep Learning
Special collection: Explainable AI for Deep Models
Workshop: Deep Learning for Medical Imaging Applications
Tutorial: Generative Models and Diffusion Techniques
Dataset release: Large-Scale Vision-Language Benchmark
Tool release: Pretrained Vision Transformer Toolkit
Survey published: Self-Supervised Learning Methods
View all News
About IRJS – Deep Learning
IRJS – Deep Learning is a specialized publication dedicated to advancing research in deep neural networks and their applications. Our journal focuses on both theoretical developments and practical implementations of deep learning across various domains.
We publish innovative research on convolutional neural networks, recurrent neural networks, transformer architectures, generative models, and the mathematical foundations of deep learning. The journal emphasizes reproducible research and practical applications.
Technical Focus
- Deep Neural Architectures
- Computer Vision Applications
- Natural Language Processing
- Generative Models & GANs
- Explainable AI & Interpretability
Journal Standards
- Indexed in Scopus & ESCI
- Rigorous Peer Review
- Code & Data Sharing Encouraged
- International Recognition
- Cutting-Edge Research