Multi-Object Datasets

Multi-Object Datasets (https://github.com/deepmind/multi_object_datasets) post-processed for easy replication of the training and evaluation of the ICML'21 paper "Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations". Datasets for CLEVR6, Multi-dSprites, and Tetrominoes are in the .h5 format and split by training and test. Images and masks are provided for each. CLEVR6 also contains the ground truth latent factors for disentanglement evaluation. Unzip the provided zip file in the desired location.

本数据集为经后处理的多目标数据集（Multi-Object Datasets，https://github.com/deepmind/multi_object_datasets），可便捷复现2021年国际机器学习大会（ICML 2021）论文《面向对称与解耦多目标表征学习的高效迭代摊销推理》（"Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations"）中的训练与评估流程。其中CLEVR6、Multi-dSprites与Tetrominoes三类数据集均以.h5格式存储，并按训练集与测试集划分，每个样本均配套提供图像与掩码。CLEVR6还额外包含用于解耦表征评估的真实潜在因子。请将提供的压缩包解压至指定路径。