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 [1].
Unsupervised Order Learning
Authors: Seon-Ho Lee, Nyeong-Ho Shin, Chang-Su Kim
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various orderable datasets demonstrate that UOL provides reliable ordered clustering results and decent rank estimation performances with no supervision. (Abstract) and This section provides various experimental results. (Section 4) |
| Researcher Affiliation | Academia | Seon-Ho Lee, Nyeong-Ho Shin & Chang-Su Kim School of Electrical Engineering, Korea University Seoul 02841, Korea EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Unsupervised Order Learning (UOL) (Page 4) |
| Open Source Code | Yes | The source codes are available at https://github.com/seon92/UOL. (Abstract) |
| Open Datasets | Yes | MORPH II (Ricanek & Tesafaye, 2006)... CLAP2015 (Escalera et al., 2015)... DR (Dugas et al., 2015)... Retina MNIST (Yang et al., 2021)... FER+ (Barsoum et al., 2016) (Section 4.2) |
| Dataset Splits | No | Setting A 5,492 images of the Caucasian race are selected and then randomly divided into two disjoint subsets: 80% for training and 20% for testing. Setting B 21,000 images... They are split into three disjoint subsets S1, S2, and S3. We use S2 for training and S1 + S3 for testing. (Appendix C.1) - This provides train/test, but no explicit validation split. |
| Hardware Specification | Yes | We do all experiments using Py Torch (Paszke et al., 2019) and an NVIDIA Ge Force RTX 3090 GPU. (Appendix C.1) |
| Software Dependencies | No | We do all experiments using Py Torch (Paszke et al., 2019) and an NVIDIA Ge Force RTX 3090 GPU. (Appendix C.1) - While PyTorch is mentioned, its specific version number is not given. |
| Experiment Setup | Yes | We initialize the encoder h with VGG16 pre-trained on ILSVRC2012 (Deng et al., 2009). We use the Adam optimizer (Kingma & Ba, 2015) with a batch size of 32 and a weight decay of 5 10 4. We set the learning rate to 10 4. For data augmentation, we do random horizontal flips and random crops. (Section 4.1) |