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..
On the choice of Perception Loss Function for Learned Video Compression
Authors: Sadaf Salehkalaibar, Truong Buu Phan, Jun Chen, Wei Yu, Ashish Khisti
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using information theoretic analysis and deep-learning based experiments, we demonstrate that the choice of PLF can have a significant effect on the reconstruction, especially at low-bit rates. ... We validate our results using (one-shot) information-theoretic analysis, detailed study of the rate-distortion-perception tradeoff of the Gauss-Markov source model as well as deep-learning based experiments on moving MNIST and KTH datasets. |
| Researcher Affiliation | Academia | Sadaf Salehkalaibar ECE Department University of Toronto EMAIL Buu Phan* ECE Department University of Toronto EMAIL Jun Chen ECE Department Mc Master University EMAIL Wei Yu ECE Department University of Toronto EMAIL Ashish Khisti ECE Department University of Toronto EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | Code will be available at https://github.com/truongbuu/URDP_flow. |
| Open Datasets | Yes | We validate our results using ... deep-learning based experiments on moving MNIST and KTH datasets. ... Moving MNIST dataset [29] (with 1 digit) using Wasserstein GAN [30] ... Additional results on the KTH dataset [31] are available in Appendix J.3. |
| Dataset Splits | No | The paper mentions 'training set contains 60000 images' but does not provide specific train/validation/test splits or a clear splitting methodology. |
| Hardware Specification | Yes | Training takes 2 days per model on a single NVIDIA P100 GPU. |
| Software Dependencies | No | The paper mentions software like 'Wasserstein GAN', 'scale-space flow model', and 'conditional module', and 'WGAN-GP framework', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use a batch size of 64, RMSProp optimizer with a learning rate of 5 10 5, and train each model with 360 epochs, where the training set contains 60000 images. ... Under WGAN-GP framework [30], we use the gradient penalty of 10 and update the encoders/decoders for every 5 iterations. The parameters λ controlling the tradeoff are in Table.7. |