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..
LICO: Explainable Models with Language-Image COnsistency
Authors: Yiming Lei, Zilong Li, Yangyang Li, Junping Zhang, Hongming Shan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on eight benchmark datasets demonstrate that the proposed LICO achieves a significant improvement in generating more explainable attention maps in conjunction with existing interpretation methods such as Grad-CAM. Remarkably, LICO improves the classification performance of existing models without introducing any computational overhead during inference. |
| Researcher Affiliation | Academia | 1 Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University 2 Academy of Mathematics and Systems Science, Chinese Academy of Sciences 3 Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University 4 Shanghai Center for Brain Science and Brain-inspired Technology |
| Pseudocode | Yes | Algorithm 1 Training Algorithm of LICO. |
| Open Source Code | Yes | Source code is made available at https://github.com/ym Lei FDU/LICO. |
| Open Datasets | Yes | This paper focuses on image classification task and evaluates the proposed LICO on well-known datasets, including Image Net-1k [32], CIFAR-10/100 [33], and SVHN [34]. |
| Dataset Splits | Yes | We conduct the classification experiments under the setting of limited training data in which the splits of labeled data follow the previous works for fair comparison [35, 36]. |
| Hardware Specification | Yes | The experiments were trained on four NVIDIA A100 GPUs for Image Net-1k and one GPU for other datasets. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | The learning rates for Image Net, CIFAR10/100, and SVHN are of 0.03 with a consine rate decay schedule, i.e., η = η0 cos( 7πk 16K ), where η0 denotes the initial learning rate and k is the index of training step [46]. We use a standard stochastic gradient descent (SGD) optimizer with a momentum of 0.9 [47, 48], and the weight decay is 0.0001. The training batch sizes are 128 and 64 for Image Net and other datasets, respectively. Specifically, the mapping net for Res Net-50 is hψ[512, 49], hψ[512, 64] for WRN, and hψ[512, 49] for PARN-18. The total training epoch is 90 for Image Net and 200 for others. |