Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Wasserstein Iterative Networks for Barycenter Estimation
Authors: Alexander Korotin, Vage Egiazarian, Lingxiao Li, Evgeny Burnaev
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop a novel iterative algorithm (M4) for estimating Wasserstein-2 barycenters... We construct the Ave, celeba! (averaging celebrity faces, M5) dataset... for large-scale quantitative evaluation... We evaluate our iterative algorithm 1 and a recent state-of-the-art variational [SCW2B] by [19] on Ave, celeba! dataset... for quantitative evaluation we use FID score [23] computed on 200K generated samples w.r.t. the original Celeb A dataset, see Table 1. Our method drastically outperforms [SCW2B]. |
| Researcher Affiliation | Academia | Alexander Korotin Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL Vage Egiazarian Skolkovo Institute of Science and Technology Moscow, Russia EMAIL Lingxiao Li Massachusetts Institute of Technology Cambridge, Massachusetts, USA EMAIL Evgeny Burnaev Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL |
| Pseudocode | Yes | Algorithm 1: Wasserstein Iterative Networks (WIN) for Barycenter Estimation |
| Open Source Code | Yes | The code1 is written on the Py Torch and includes the script for producing Ave, celeba! dataset. 1https://github.com/iamalexkorotin/Wasserstein Iterative Networks |
| Open Datasets | Yes | We use Celeb A 64 64 faces dataset [36] as the basis for our Ave, celeba! dataset... 50K Shoes [68], 138K Amazon Handbags and 90K Fruits [45]. |
| Dataset Splits | No | The paper describes creating the Ave, celeba! dataset by splitting images into 3 parts for barycenter calculation, and mentions using mini-batches for training, but does not provide explicit train/validation/test splits for the generative model (Gξ) itself. |
| Hardware Specification | Yes | The experiments are conducted on 4 GPU GTX 1080ti. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | In practice, we choose KG = 50 as it empirically works better... Input :latent S and input P1, . . . , PN measures; weights α1, . . . , αN > 0 (PN n=1 αn = 1); number of iters per network: KG, KT , Kv; generator Gξ : RH RD; mapping networks Tθ1, . . . , TθN : RD RD; potentials vω1, . . . , vωN : RD R; regression loss ℓ: RD RD R+;... In Lemma 3, we set N = 3, M = 2, β1 = β2 = 1/2, w1 = w2 = 1/2, (γl) = 1 0 0 0 1 0 , (γr) = 0 1 0 0 0 1 which yields weights (α1, α2, α3) = (1/4, 1/2, 1/4). |