Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Authors: Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement two variants of our model and demonstrate their efficacy on MNIST, Fashion-MNIST and Celeb A. Altogether, we design a novel conditioning framework that can be used with any architecture to synthesize unseen attribute combinations. |
| Researcher Affiliation | Collaboration | 1Department of Computing, Imperial College London, United Kingdom 2Department of Informatics and Telecommunications, University of Athens, Greece. |
| Pseudocode | No | The paper describes methods using mathematical equations and prose, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | All models were implemented in Pytorch (Paszke et al., 2017) and Tensorly (Kossaifiet al., 2019). No explicit statement about releasing their own code or a link to it is provided. |
| Open Datasets | Yes | To evaluate our model on multi-attribute conditional image generation, we perform experiments on the MNIST (Le Cun et al., 1998), Fashion-MNIST (Xiao et al., 2017) and Celeb A (Liu et al., 2015) datasets. |
| Dataset Splits | Yes | The MNIST dataset consists of 60k training images and 10k test images of handwritten digits. Fashion-MNIST consists of 60k training and 10k test images. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | Yes | All models were implemented in Pytorch (Paszke et al., 2017) and Tensorly (Kossaifiet al., 2019). |
| Experiment Setup | Yes | For the experiments on MNIST and Fashion-MNIST the encoder and decoder networks have 4 layers, while the networks for Celeb A have 5 layers. All label decoders are affine transformations. We set β 1 for all experiments, except for Celeb A where we set β 10. For fair comparison we train all models for 50 epochs using the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 0.0005. |