Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Authors: Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |