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Causes of Similarity-Dissimilarity Effect

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...

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相关实验视频

Updated: Jan 15, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

通过数据集相似性通过深度神经网络参数选择在元学习框架下.

Liping Deng, Maziar Raissi, MingQing Xiao

IEEE transactions on pattern analysis and machine intelligence

|October 7, 2025

概括

这项研究引入了一个新的元学习框架,通过使用数据集相似性,共同推深度神经网络 (DNN) 的超参数和初始权重. 这种方法提高了深度学习模型的适应性和有效性.

科学领域:

人工智能的人工智能
机器学习机器学习
计算机视觉计算机视觉

背景情况:

深度神经网络 (DNN) 的性能对超参数选择和重量初始化非常敏感.
当前的方法通常会独立优化这些因素,从而限制模型的适应性和有效性.

研究的目的:

开发一个新的元学习框架,共同推超参数和初始权重.
为了利用数据集相似性来改进深度学习模型的优化.

主要方法:

从历史数据集中提取元特征 (浅层和深层).
在查询数据集的元特征空间中计算数据集相似性.
基于类似的历史数据集推参数配置.

主要成果:

该框架在105个现实世界图像分类任务中进行了评估.
对于视觉变压器和卷积神经网络,在最先进的基线上表现出一致的超越性.
验证了数据集驱动参数推的有效性.

结论:

通过数据集相似性共同推超参数和初始权重是有效的.
拟议的元学习框架提高了深度学习模型的性能.

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Last Updated: Jan 15, 2026

Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

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Published on: December 15, 2023

这种数据集驱动的方法提供了更具适应性和有效的优化策略.