TinyImageNet statistics and PCA
收藏NIAID Data Ecosystem2026-05-02 收录
下载链接：
https://zenodo.org/record/14589100
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资源简介：
TinyImageNet mean (mean_reshaped.npy) and standard deviation (std_reshaped.npy) calculated on the training set;
TinyImageNet's covariance matrix's eigenvalues (eigenvalues.npy), the ratio of total variance explained by each principal component (eigenvalues_ratio.npy) and TinyImageNet's principal components (pc_matrix.npy) computed using the normalized training dataset.
These items were used in [1]. The TinyImageNet dataset was presented in [2].
[1] Alice Bizeul, Thomas M. Sutter, Alain Ryser, Julius Von Kügelgen, Bernhard Schölkopf, Julia E. Vogt. Components Beat Patches: Eigenvector Masking for Visual Representation Learning. Oct, 2024.
[2] Le, Ya, and Xuan Yang. "Tiny imagenet visual recognition challenge." CS 231N 7.7 (2015): 3.
创建时间：
2025-01-02
