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..
Unsupervised Disentangled Representation Learning with Analogical Relations
Authors: Zejian Li, Yongchuan Tang, Yongxing He
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 we demonstrate the experiment results and compare our method with other methods along the subspace score. In this section, we present the experiment results on five image datasets, including MNIST [Le Cun et al., 1998], Celeb A [Liu et al., 2015], Flower [Nilsback and Zisserman, 2008], CUB [Wah et al., 2011] and Chairs [Aubry et al., 2014]. Specifically, we compare our methods with other state-of-the-art methods along the subspace score. Table 1 reports the subspace score of models. |
| Researcher Affiliation | Academia | Zejian Li, Yongchuan Tang , Yongxing He College of Computer Science, Zhejiang University, Hangzhou 310027, China EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code will be available on https://github.com/Zejian Li/analogical-training. |
| Open Datasets | Yes | In this section, we present the experiment results on five image datasets, including MNIST [Le Cun et al., 1998], Celeb A [Liu et al., 2015], Flower [Nilsback and Zisserman, 2008], CUB [Wah et al., 2011] and Chairs [Aubry et al., 2014]. |
| Dataset Splits | No | The paper mentions usage of 'train' and 'test' in the context of models and results, but does not provide specific train/validation/test dataset splits with percentages, counts, or a clear splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'scikit-learn' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For Ana GAN, we use the network architecture of DCGAN [Radford et al., 2015]. We use the WGAN-GP loss [Gulrajani et al., 2017] instead of the original GAN loss and the parameter num critic is set as 3. At the first 100 epoch, we only optimize V (D, G)... Both the noise and the code are sampled from the standard normal distribution. We use Adam optimizer [Kingma and Ba, 2014] with a learning rate of 0.00002 and a momentum of 0.5. The batch size is 32. Ana VAE shares most of the configuration in Ana GAN. The encoder network Q borrows the major structure of D in Ana GAN. The learning rate for the Adam optimizer is 0.0001... We add a dropout layer [Srivastava et al., 2014] after each nonlinear activation layer in R to avoid overfitting. To compute the subspace score, a cluster of ten sample sequences is generated for each factor and each sequence has five samples. The sequence is generated by varying the corresponding component of the code from -2 to 2 with the interval 1 but keeping other components fixed. We compute the subspace score over five different sets of generated samples to get the average. |