zozonext/shift15m

Dataset Description

SHIFT15M was introduced as a benchmark for evaluating set-to-set matching models when the training and test distributions differ. Many machine learning methods assume that training and test data are independently and identically distributed, but this assumption is often violated in real-world applications. In fashion, trends change over time, causing shifts in item appearance, prices, user preferences, and outfit composition.

The dataset supports experiments under several types of distribution shifts, including covariate shift and target shift. It also provides multiple benchmark tasks, such as regression, classification, and set-to-set matching.

Supported Tasks

SHIFT15M supports the following tasks:

Task	Task Type	Shift Type	Input	Output
NumLikesRegression	Regression	Target shift	(N, 25)	(N, 1)
SumPricesRegression	Regression	Covariate shift / Target shift	(N, 1)	(N, 1)
ItemPriceRegression	Regression	Target shift	(N, 4096)	(N, 1)
ItemCategoryClassification	Classification	Target shift	(N, 4096)	(N, 7)
Set2SetMatching	Set-to-set matching	Covariate shift	(N, 4096) × (M, 4096)	(1)

These tasks are provided through the official software package for handling the dataset.

Citation

@INPROCEEDINGS {10208629,
author = { Kimura, Masanari and Nakamura, Takuma and Saito, Yuki },
booktitle = { 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) },
title = {{ SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts }},
year = {2023},
pages = {3508-3513},
}