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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 2061–2070 of 9051 papers

TitleStatusHype
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Conceptual Mapping of Controversies—0
A high fidelity synthetic face framework for computer vision—0
Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons—0
Conceptual capacity and effective complexity of neural networks—0
Active learning for interactive satellite image change detection—0
Conceptors: an easy introduction—0
Concept-Monitor: Understanding DNN training through individual neurons—0
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