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..
Label Enhancement for Label Distribution Learning via Prior Knowledge
Authors: Yongbiao Gao, Yu Zhang, Xin Geng
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed approach outperforms the state-of-the-art methods in both age estimation and image emotion recognition. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, Southeast University, Nanjing, China EMAIL |
| Pseudocode | No | The paper describes its methodology in text and mathematical formulas but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the availability of open-source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | Two datasets are used in this application. The ο¬rst one is the FG-NET Aging dataset [Lanitis et al., 2002]... The second dataset is the much larger MORPH dataset [Ricanek and Tesafaye, 2006]... We execute our experiments on two image emotion distribution datasets, Flickr LDL and Twitter LDL [Yang et al., 2017b] |
| Dataset Splits | No | We randomly select 80% for training and the remaining 20% for testing. The paper does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions deep learning models like VGGNET and algorithms like Q-learning, but does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | For both models, the learning rate is 0.001, the batch size is 64, the discount factor Ξ³ is 0.9. And the size of the prioritized replay is 5000. We use the Ο΅ greedy method to select the action for exploration. |