Schrodinger Bridge Flow for Unpaired Data Translation
Authors: Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |