Upsampling Layer

Upsampling layers in deep learning increase the resolution of feature maps, a crucial step in many applications like image generation, point cloud processing, and speech enhancement. Current research focuses on improving the quality and efficiency of upsampling, addressing issues like artifacts (e.g., tonal or filtering artifacts in audio), boundary inconsistencies, and the need for context-aware upsampling to maintain semantic coherence. These improvements are vital for enhancing the performance and interpretability of various deep learning models across diverse fields, leading to better results in tasks ranging from image restoration to industrial defect segmentation.