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..
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Authors: Ye Du, Yujun Shen, Haochen Wang, Jingjing Fei, Wei Li, Liwei Wu, Rui Zhao, Zehua Fu, Qingjie LIU
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the effectiveness and superiority of our approach under a wide range of settings. |
| Researcher Affiliation | Collaboration | Ye Du1,2 Yujun Shen3 Haochen Wang4 Jingjing Fei5 Wei Li5 Liwei Wu5 Rui Zhao5,6 Zehua Fu1,2 Qingjie Liu1,2 1 State Key Laboratory of Virtual Reality Technology and Systems, Beihang University 2 Hangzhou Innovation Institute, Beihang University 3 The Chinese University of Hong Kong 4 Institute of Automation, Chinese Academy of Sciences 5 Sense Time Research 6 Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | Yes | We provide pseudo-codes to further illustrate how we implement Eq. (4) in Supplementary Material. |
| Open Source Code | Yes | Code is available at https://github.com/usr922/FST. |
| Open Datasets | Yes | In UDA segmentation, we use synthetic labeled images from GTAV [38] and SYNTHIA [39] as the source domain and use real images from Cityscapes [13] as the target domain. In addition, PASCAL VOC 2012 [15] is used for standard semi-supervised evaluation. |
| Dataset Splits | Yes | To simulate a semi-supervised setting, we randomly sample a portion (i.e., 1/4, 1/8, and 1/16) of images together with corresponding segmentation masks from the training set as the labeled data and treat the rest as the unlabeled samples. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions optimizers (AdamW, SGD) and network architectures (Deep Lab V2, Deep Lab V3+, PSPNet, UPer Net) but does not provide specific version numbers for any software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | In UDA segmentation, the model is trained with an Adam W [28] optimizer, a learning rate of 6 × 10−5 for the encoder and 6 × 10−4 for the decoder, a weight decay of 0.01, linear learning rate warmup with 1.5k iterations and linear decay afterwards. We train the model on a batch of two 512 × 512 random crops for a total of 40k iterations. The momentum u is set to 0.999. In semi-supervised segmentation, the model is trained with a SGD optimizer, a learning rate of 0.0001 for the encoder and 0.001 for the decoder, a weight decay of 0.0001. We train the model with 16 labeled and 16 unlabeled images per-batch for a total of 40 epochs. |