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Found 5 papers, 2 papers with code
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
In this paper, we propose a novel framework, dubbed Detaux, whereby a weakly supervised disentanglement procedure is used to discover new unrelated classification tasks and the associated labels that can be exploited with the principal task in any Multi-Task Learning (MTL) model.
On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper
The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year.
The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores.
Short-observation new product sales forecasting
Time Series Analysis
Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends
In particular, we propose a neural network-based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information.
Ranked #3 on
New Product Sales Forecasting
on VISUELLE
Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering
This problem statement is similar to that of "biclustering", implying that RBC can be cast as a biclustering problem.
