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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Schrodinger Bridge Flow for Unpaired Data Translation
Authors: Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our algorithm on a variety of unpaired data translation tasks. |
| Researcher Affiliation | Industry | Valentin De Bortoli Google Deep Mind Iryna Korshunova Google Deep Mind Andriy Mnih Google Deep Mind Arnaud Doucet Google Deep Mind |
| Pseudocode | Yes | Algorithm 1 α-Diffusion Schrödinger Bridge Matching |
| Open Source Code | No | Due to IP restrictions, we cannot share the codebase used for this paper. However, we plan to release some notebooks in order to reproduce experiments in a small scale setting. |
| Open Datasets | Yes | We closely follow the setup of Shi et al. (2023) and De Bortoli et al. (2021), and train the models to transfer between 10 EMNIST letters, A-E and a-e, and 10 MNIST digits (CC BY-ND 4.0 license). [...] We consider the problem of image translation between Cat and Wild domains of AFHQ (Choi et al. (2020); CC BY-NC 4.0 DEED licence). |
| Dataset Splits | Yes | For DSBM finetuning, we perform 30 outer iterations, i.e. alternating between training the forward and the backward networks, while at each outer iteration a network is trained for 5000 steps. [...] For evaluation, we compute FID based on the whole MNIST training set of 60000 examples and a set of 4000 samples that were initialised from each test image in the EMNIST dataset. |
| Hardware Specification | Yes | Pretraining a bidirectional model on 4 v3 TPUs takes 1 hour, while the online finetuning stage requires 4 hours on 16 v3 TPUs. |
| Software Dependencies | No | To optimise our networks, we use Adam (Kingma and Ba, 2015) with β = (0.9, 0.999)... The paper mentions Adam optimizer with its parameters but does not specify versions for programming languages, libraries (e.g., PyTorch, TensorFlow), or other key software components. |
| Experiment Setup | Yes | For every model used in the paper, we provide hyperparameters in Table 3. [...] To optimise our networks, we use Adam (Kingma and Ba, 2015) with β = (0.9, 0.999), and we modify the gradients to keep their global norm below 1.0. We re-initialise the optimiser s state when the finetuning phase starts. |