Adversarially Learned Inference
Authors: Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With experiments on the Street View House Numbers (SVHN) dataset (Netzer et al., 2011), the CIFAR-10 object recognition dataset (Krizhevsky & Hinton, 2009), the Celeb A face dataset (Liu et al., 2015) and a downsampled version of the Image Net dataset (Russakovsky et al., 2015), we show qualitatively that we maintain the high sample fidelity associated with the GAN framework, while gaining the ability to perform efficient inference. We show that the learned representation is useful for auxiliary tasks by achieving results competitive with the state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks. |
| Researcher Affiliation | Academia | 1 MILA, Université de Montréal, firstname.lastname@umontreal.ca. 2 Neural Dynamics and Computation Lab, Stanford, poole@cs.stanford.edu. 3 New York University, martinarjovsky@gmail.com. |
| Pseudocode | Yes | Algorithm 1 The ALI training procedure. |
| Open Source Code | Yes | The code for all experiments can be found at https://github.com/IshmaelBelghazi/ALI. |
| Open Datasets | Yes | With experiments on the Street View House Numbers (SVHN) dataset (Netzer et al., 2011), the CIFAR-10 object recognition dataset (Krizhevsky & Hinton, 2009), the Celeb A face dataset (Liu et al., 2015) and a downsampled version of the Image Net dataset (Russakovsky et al., 2015). |
| Dataset Splits | Yes | A 10,000 example held-out validation set is taken from the training set and is used for model selection. |
| Hardware Specification | No | The paper does not specify any particular hardware used for experiments, such as GPU models, CPU types, or cloud computing instances with detailed specifications. |
| Software Dependencies | Yes | We would also like to thank the developers of Theano (Bergstra et al., 2010; Bastien et al., 2012; Theano Development Team, 2016), Blocks and Fuel (van Merriënboer et al., 2015). |
| Experiment Setup | Yes | A HYPERPARAMETERS ... Table 3: CIFAR10 model hyperparameters (unsupervised). ... Optimizer Adam (α = 10 4, β1 = 0.5, β2 = 10 3) Batch size 100 Epochs 6475 Leaky Re LU slope, maxout pieces 0.1, 2 Weight, bias initialization Isotropic gaussian (µ = 0, σ = 0.01), Constant(0) |