Remove Categorization Remove Data Scarcity Remove Explainability
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Convolutional Neural Networks: A Deep Dive (2024)

Viso.ai

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.

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Computer Vision Tasks (Comprehensive 2024 Guide)

Viso.ai

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.

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N-Shot Learning: Zero Shot vs. Single Shot vs. Two Shot vs. Few Shot

Viso.ai

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.

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Small but Mighty: The Enduring Relevance of Small Language Models in the Age of LLMs

Marktechpost

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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