Viso.ai

JANUARY 2, 2024

Deep Dive: Convolutional Neural Network Algorithms for Specific Challenges CNNs, while powerful, face distinct challenges in their application, particularly in scenarios like data scarcity, overfitting, and unstructured data environments. Making CNN models more interpretable and explainable.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

Viso.ai

DECEMBER 6, 2023

Image Classification Image classification tasks involve CV models categorizing images into user-defined classes for various applications. Based on the presence of a tiger, the entire image is categorized as such. Semantic Segmentation Semantic segmentation aims to identify each pixel within an image for a more detailed categorization.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Categorization 52

Viso.ai

JANUARY 16, 2024

Also, you can use N-shot learning models to label data samples with unknown classes and feed the new dataset to supervised learning algorithms for better training. The AI community categorizes N-shot approaches into few, one, and zero-shot learning. As the article explains, the N-shot learning paradigms address these data challenges.

Neural Network 52

Neural Network Computer Vision Convolutional Neural Networks Algorithm 52

Marktechpost

SEPTEMBER 15, 2024

These sources can be categorized into three types: textual documents (e.g., KD methods can be categorized into white-box and black-box approaches. In high-stakes decision-making contexts, easily auditable and explainable models are typically favored.

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BERT LLM Large Language Models Categorization 120