Supervised Learning has been dominant for years, but its reliance on labeled data—a costly and time-consuming resource—creates challenges, especially in areas like medical imaging. On the other hand, ...
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The Annotated NeRF – Training on Custom Dataset from Scratch in Pytorch
In recent years, the field of 3D from multi-view has become one of the most popular topics in computer vision conferences, with a high number of submitted papers each year. A groundbreaking paper in ...
Stable Diffusion 3.5: Paper Explanation and Inference
Stable Diffusion 3.5, released on June 2024 by Stability AI, is the third iteration in the Stable Diffusion family. The Turbo-Large and Large variants of the SD3.5 family are Stability AI’s most ...
LightRAG: Simple and Fast Alternative to GraphRAG for Legal Doc Analysis
LightRAG is an innovative approach based on GraphRAG that combines the attributes of Knowledge Graphs with embedding-based retrieval systems, making it fast as well as performant, achieving SOTA ...
Introduction to Speech to Speech: Most Efficient Form of NLP
We often take out our phones and say, “Hey Siri, play Perfect by Ed Sheeran” or “Ok Google, set an alarm at 7.30 in the morning.” And the work is done on the flow by our phones! But have you ever ...
Training 3D U-Net for Brain Tumor Segmentation Challenge – Medical Imaging
3D U-Net, a powerful deep learning architecture for medical image segmentation, is designed to process 3D volumetric data like brain tumors, enabling a more comprehensive and precise analysis of brain ...