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..
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
Authors: Đ.Khuê Lê-Huu, Karteek Alahari
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate this in our empirical results on standard semantic segmentation datasets, where several instantiations of our regularized Frank-Wolfe outperform mean field inference, both as a standalone component and as an end-to-end trainable layer in a neural network. |
| Researcher Affiliation | Academia | Ð.Khuê Lê-Huu Karteek Alahari Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK 38000 Grenoble, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Generic regularized Frank-Wolfe for (approximately) solving MAP inference (6). |
| Open Source Code | Yes | Our source code is made publicly available under the GNU general public license for this purpose.1 1https://github.com/netw0rkf10w/CRF |
| Open Datasets | Yes | We first pretrain Deep Labv3 and Deep Labv3+ on the COCO dataset [46] and then finetune them on PASCAL VOC (trainaug) and Cityscapes (train) to obtain similar results to previous work [16, 17] (Table 1, CNN column). |
| Dataset Splits | Yes | We first pretrain Deep Labv3 and Deep Labv3+ on the COCO dataset [46] and then finetune them on PASCAL VOC (trainaug) and Cityscapes (train) to obtain similar results to previous work [16, 17] (Table 1, CNN column). ...Table 1 shows the performance on the validation sets of PASCAL VOC and Cityscapes... |
| Hardware Specification | No | The paper states: 'The experiments were performed using HPC resources from GENCI-IDRIS (Grants 2020-AD011011321 and 2020AD011011881).' However, it does not specify concrete hardware details such as specific GPU or CPU models, memory sizes, or detailed cloud instance types used for the experiments. |
| Software Dependencies | Yes | Our implementation builds on PyTorch 1.7.0 and mmsegmentation [2]. |
| Experiment Setup | Yes | We train the model for 20 epochs with 5 CRF iterations, using the same poly schedule as before. ...We set its learning rate to a small value of 0.0001. For the CRF, we tried 4 different values of initial learning rates 0 2 {1.0, 0.1, 0.01, 0.001}... |