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..
Contrastive Adversarial Learning for Person Independent Facial Emotion Recognition
Authors: Daeha Kim, Byung Cheol Song5948-5956
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the proposed adversarial learning scheme was theoretically verified, and it was experimentally proven to show state of the art (SOTA) performance.Quantitative evaluation: Table 1 shows the experimental results for the Affect Net dataset. The proposed method is better than the latest algorithms such as FHC (Kossaifiet al. 2020) and Bre G-Ne Xt (Hasani, Negi, and Mahoor 2020). |
| Researcher Affiliation | Academia | Daeha Kim, Byung Cheol Song Department of Electronic Engineering, Inha University, Incheon 22212, South Korea EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 describes the overview of CAF. |
| Open Source Code | Yes | Software is available at https://github.com/kdhht2334/ Contrastive-Adversarial-Learning-FER |
| Open Datasets | Yes | Affect Net (Mollahosseini, Hasani, and Mahoor 2017) dataset consists of over a million images... AFEW-VA (Kossaifiet al. 2017) dataset is derived from the AFEW (Dhall et al. 2016) dataset... Aff-Wild (Zafeiriou et al. 2017) dataset consists of about 300 videos... |
| Dataset Splits | Yes | The test dataset is not released. So, a part of the training dataset is randomly selected and used as an evaluation dataset in this paper, same as (Hasani, Negi, and Mahoor 2020). |
| Hardware Specification | Yes | All experiments were performed on the Intel Xeon CPU and Ge Force GTX 1080 TI, with five training sessions per experiment 1. |
| Software Dependencies | No | The paper mentions using Adam optimizer and Alex Net/Res Net18 as backbones, but does not provide specific version numbers for software libraries like Python, PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | Yes | Encoder, critic, and FC layers were optimized through the learning rate of Adam (Kingma and Ba 2014) optimizer with 1e-4. The minibatch size of Alex Net and Res Net18 were set to 256 and 128, respectively. The parameters were updated through 50,000 iterations for the Affect Net and AFEW-VA datasets, and 100,000 iterations for the Aff-Wild dataset. We reduced the learning rate by 0.8 times every 10k iterations. |