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..
Few-Cost Salient Object Detection with Adversarial-Paced Learning
Authors: Dingwen Zhang, HaiBin Tian, Jungong Han
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on four widely-used benchmark datasets demonstrate that the proposed method can effectively approach to the existing supervised deep salient object detection models with only 1k human-annotated training images. |
| Researcher Affiliation | Academia | 1School of Mechano-Electronic Engineering, Xidian University, Xi an, Shaanxi 710071 2Computer Science Department, Aberystwyth University, Ceredigion, SY23 3FL |
| Pseudocode | No | The paper refers to "Alg. 1 and Alg. 2" as showing the optimization pipeline, but these algorithm blocks are not present in the provided text. |
| Open Source Code | Yes | The project page is available at https://github.com/hb-stone/FC-SOD. |
| Open Datasets | Yes | We use four widely-used benchmark datasets to implement the experiments, which include PASCALS [41], DUT-O [42], SOD [43], and DUTS [28]. Following the previous works [44, 20, 45], we use the training split of the DUT-S dataset for training and test the trained models on the other datasets. |
| Dataset Splits | No | Following the previous works [44, 20, 45], we use the training split of the DUT-S dataset for training and test the trained models on the other datasets. The paper specifies training and test splits but does not explicitly detail a separate validation split or its size/proportion. |
| Hardware Specification | Yes | We implement the proposed algorithm on the Py Torch framework using a NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | We implement the proposed algorithm on the Py Torch framework using a NVIDIA GTX 1080Ti GPU. The paper mentions PyTorch but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | When training the saliency network, we use the Stochastic Gradient Descent (SGD) optimization method, where the momentum is set to 0.9, and the weight decay is set to 5 10 4. The initial learning rates of the taskpredictor and the pace-generator are 2.5 10 4 and 10 4, respectively, which are decreased with polynomial decay parameterized by 0.9. For training the pace network, we adopt the Adam optimizer [46] with the learning rate 10 4. The same polynomial decay as the saliency network is also used. We set beta = 0.01 and eta = 0.7 according to a heuristic grid search process. our method uses in total 24.5K iterations and the loss and performance curves are shown in Fig. 2. |