Multimodal Generative Models for Scalable Weakly-Supervised Learning
Authors: Mike Wu, Noah Goodman
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experiments to measure the quality of the MVAE, comparing with previous models. We train on MNIST [14], binarized MNIST [13], Multi MNIST [6, 20], Fashion MNIST [30], and Celeb A [15]. Several of these datasets have complex modalities character sequences, RGB images requiring large inference networks with RNNs and CNNs. We show that the MVAE is able to support heavy encoders with thousands of parameters, matching state-of-the-art performance. |
| Researcher Affiliation | Academia | Mike Wu Department of Computer Science Stanford University Stanford, CA 94025 wumike@stanford.edu Noah Goodman Departments of Computer Science and Psychology Stanford University Stanford, CA 94025 ngoodman@stanford.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We train on MNIST [14], binarized MNIST [13], Multi MNIST [6, 20], Fashion MNIST [30], and Celeb A [15]. |
| Dataset Splits | No | The paper mentions training, but does not explicitly provide training/validation/test dataset splits needed to reproduce the experiment, such as specific percentages or sample counts for each split. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions using Adam and RNN architectures from [2] but does not provide specific version numbers for software components or libraries. |
| Experiment Setup | Yes | We use Adam with a 10 4 learning rate, a batch size of 50, λi = 1 for i = 1, ..., N, β annealing for 20 out of 100 epochs. |