Single Stage

Single-stage approaches in various machine learning tasks aim to simplify complex processes by performing all necessary operations within a single network, eliminating the need for multiple sequential stages. Current research focuses on developing efficient single-stage architectures, such as transformers and GAN variants, for diverse applications including image denoising, object detection, speech-to-image generation, and semantic segmentation. This focus on single-stage methods improves computational efficiency and reduces error propagation compared to multi-stage alternatives, leading to advancements in areas like computer vision, natural language processing, and reinforcement learning.