Source Separation with Deep Generative Priors
Authors: Vivek Jayaram, John Thickstun
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The method achieves state-of-the-art performance for MNIST digit separation. We introduce new methodology for evaluating separation quality on richer datasets, providing quantitative evaluation of separation results on CIFAR-10. We also provide qualitative results on LSUN. 5. Experiments We evaluate results of BASIS on 3 datasets: MNIST (Le Cun et al., 1998) CIFAR-10 (Krizhevsky, 2009) and LSUN (Yu et al., 2015). |
| Researcher Affiliation | Academia | * Equal contribution. Paul G. Allen School of Computer Science and Engineering, University of Washington. Correspondence to: Vivek Jayaram <vjayaram@cs.washington.edu>, John Thickstun <thickstn@cs.washington.edu>. |
| Pseudocode | Yes | Algorithm 1 BASIS Separation Input: m X, {σi}L i=1, δ, T Sample x1, . . . , xk Uniform(X) for i 1 to L do ηi δ σ2 i /σ2 L for t = 1 to T do Sample εt N(0, I) u(t) x(t) + ηi x log pσi(x(t)) + 2ηεt x(t+1) u(t) ηi σ2 i Diag(α) m g(x(t)) end for end for |
| Open Source Code | Yes | Code and instructions for reproducing these experiments is available online.1 1https://github.com/jthickstun/basis-separation |
| Open Datasets | Yes | We evaluate results of BASIS on 3 datasets: MNIST (Le Cun et al., 1998) CIFAR-10 (Krizhevsky, 2009) and LSUN (Yu et al., 2015). |
| Dataset Splits | No | The paper mentions evaluating results on specific numbers of separations (e.g., '6,000 pairs of equally mixed MNIST images', '25,000 separations (50,000 separated images) of two overlapping CIFAR-10 images') which are test evaluations. It does not explicitly state the train/validation/test data splits for the datasets used to train the models mentioned, nor for fine-tuning, beyond implicitly using test sets for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions generative models (NCSN, Glow) but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We adopt the hyper-parameters proposed by Song & Ermon (2019) for annealing σ2, the proportionality constant δ, and the iteration count T. The noise σ is geometrically annealed from σ1 = 1.0 to σL = 0.01 with L = 10. We set δ = 2 · 10−5, and T = 100. We find that the same proportionality constant between σ2 and η also works well for γ2 and η, allowing us to set γ2 = σ2. |