Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
Authors: Sajad Norouzi, David J. Fleet, Mohammad Norouzi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness of Exemplar VAEs on density estimation and representation learning. Importantly, generative data augmentation using Exemplar VAEs on permutation invariant MNIST and Fashion MNIST reduces classification error from 1.17% to 0.69% and from 8.56% to 8.16%. |
| Researcher Affiliation | Collaboration | 1University of Toronto, 2Vector Institute, 3Google Research |
| Pseudocode | No | The paper describes the generative process of the Exemplar VAE in three steps in Section 3, but this is presented as descriptive text rather than a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at https://github.com/sajadn/Exemplar-VAE. |
| Open Datasets | Yes | We use four datasets, namely, MNIST, Fashion-MNIST, Omniglot, and Celeb A |
| Dataset Splits | Yes | Figure 2: Training and validation ELBO on Dynamic MNIST for Exemplar VAE with and without LOO. |
| Hardware Specification | No | The paper mentions training 'convolutional models' and 'convolutional architectures' but does not specify any particular GPU or CPU models, memory sizes, or other specific hardware components used for the experiments. |
| Software Dependencies | No | The paper mentions using 'gradient normalized Adam [31, 58]' as an optimizer, but it does not specify any software versions for programming languages (e.g., Python), deep learning frameworks (e.g., PyTorch, TensorFlow), or other libraries. |
| Experiment Setup | Yes | We use gradient normalized Adam [31, 58] with learning rate 5e-4 and linear KL annealing for 100 epochs. |