Geometric Enclosing Networks
Authors: Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Phung
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments on synthesis and real-world datasets to illustrate the behaviors, strength and weakness of our proposed GEN, in particular its ability to handle multi-modal data and quality of generated data. |
| Researcher Affiliation | Academia | Trung Le1, Hung Vu2, Tu Dinh Nguyen1 and Dinh Phung1 1 Faculty of Information Technology, Monash University 2 Center for Pattern Recognition and Data Analytics, Deakin University, Australia |
| Pseudocode | Yes | Algorithm 1 Algorithm for GEN. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is publicly available. |
| Open Datasets | Yes | The popular MNIST dataset [Lecun et al., 1998] contains 60, 000 images of digits from 0 to 9. Our experiments were further extended to generating color images of real-life objects (CIFAR-10 [Krizhevsky, 2009]) and human faces (Celeb A [Liu et al., 2015]). The Frey Face dataset [Roweis and Saul, 2000] contains approximately 2000 images of Brendan s face, taken from sequential frames of a small video. |
| Dataset Splits | No | The paper mentions training on subsets of MNIST (1,000, 5,000, and 60,000 images) and other datasets but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) to reproduce the partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions the 'ADAM optimizer' and 'neural network' but does not provide specific version numbers for any programming languages, libraries, or solvers used in the implementation. |
| Experiment Setup | Yes | Unless otherwise specified, in all our experiments, when stochastic gradient descent was used, the ADAM optimizer [Kingma and Ba, 2014] with learning rate empirically turned by around 1e-3 and 1e-4 will be employed. The neural network specification for our generator G (z) includes 2 hidden layers, each with 30 softplus units (and D = 100 for the number of random features, cf. Eq. (7)) and z Uni( 1, 1). our generator G (z) has the architecture of 1, 000 1, 000 1, 000 1, 000 (softplus units) and 784 sigmoid output units; and D = 5, 000 random features was used to construct Φ. we used a convolutional generator network with 512 256 128 1, 020 (rectified linear units) and sigmoid output units and trained a leaky rectified linear discriminator network with 3 layers 32 64 128. |