Zalando Research ORG

A MNIST-like fashion product database. Benchmark :point_down:

A dataset of 70,000 clothing images in 10 categories designed as a harder drop-in replacement for the classic MNIST handwritten digit dataset. Loads directly in PyTorch, TensorFlow, and Keras with no manual download needed.

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

PyTorch implementation of VQ-VAE by Aäron van den Oord et al.

Dilated RNNs in pytorch

Periodic Spatial Generative Adversarial Networks

Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Spatial Generative Adversarial Networks

Disentangling Multiple Conditional Inputs in GANs

A Multi-modal Corpus of Fashion Images and Descriptions in German

Multilingual NLP annotation projection

State space modeling with recurrent neural networks

Create images and texts with the First Order Generative Adversarial Networks arxiv.org/abs/1802.04591

Data for and description of the ACIC 2023 data competition

Set-Equivariant Deep Learning Models

A probabilistic graphical model for COVID-19 infection spread through a population based on mutual contacts between pairs of individuals across time as well as test outcomes The C++/Python implementation enables full inference at the scale of millions of contacts between thousands of individuals.

Model of deep learning libraries

This project contains accompanying code for Zalando Team BART's (Batching Algorithms) publication Joint Order Selection, Allocation, Batching and Picking for Large Scale Warehouses.

Workshop on Recommender Systems in Fashion

Syntax-aware language models in Pytorch

RKHS feature vectors, operators, and statistical models using JAX for automatic differentiation

No description.

Very Long RNN - memory efficient computation of very long sequences in recurrent neural networks

Zalando Research Website

ICTIR Paper

Fashion CUT: Boosting Clothing Pattern Classification with Synthetic Data

The Data Cards Playbook helps dataset producers and publishers adopt a people-centered approach to transparency in dataset documentation.