Neural Separation of Observed and Unobserved Distributions
Authors: Tavi Halperin, Ariel Ephrat, Yedid Hoshen
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on audio and image separation tasks show that our method outperforms current methods that use the same level of supervision, and often achieves similar performance to full supervision. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel 2Google Research 3Facebook AI Research. |
| Pseudocode | Yes | Algorithm 1 Neural Egg Separation (NES) |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided or include a link to a code repository. |
| Open Datasets | Yes | We split the MNIST dataset (Le Cun & Cortes, 2010) [...] Handbags (Zhu et al., 2016) and Shoes (Yu & Grauman, 2014) datasets [...] Oxford-BBC Lip Reading in the Wild (LRW) Dataset (Chung & Zisserman, 2016) [...] ESC-50 (Piczak, 2015) [...] MUSDB18 Dataset (Rafii et al., 2017) |
| Dataset Splits | No | The paper describes training and testing sets, for instance, for MNIST: 'We use 12k B training images as the B training set, while for each of the other 12k B training images, we randomly sample a X image and additively combine the images to create the Y training set. We evaluate the performance of our method on 5000 Y images similarly created from the test set of X and B.' However, it does not explicitly define a validation set or a comprehensive train/validation/test split methodology for all experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU or CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | For optimization, we use SGD using ADAM update with a learning rate of 0.001. In total we perform N = 10 iterations, each consisting of optimization of T and estimation of xt, P = 25 epochs are used for each optimization of Eq. 3. |