Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
Authors: Divyansh Srivastava, Ge Yan, Lily Weng
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 26,157 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,258 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations across five standard benchmarks show that our method, VLG-CBM, outperforms existing methods by at least 4.27% and up to 51.09% on Accuracy at NEC=5 (denoted as ANEC-5), and by at least 0.45% and up to 29.78% on average accuracy (denoted as ANEC-avg), while preserving both faithfulness and interpretability of the learned concepts as demonstrated in extensive experiments. |
| Researcher Affiliation | Academia | Divyansh Srivastava , Ge Yan , Tsui-Wei Weng EMAIL UC San Diego |
| Pseudocode | No | The paper describes its pipeline and methods in prose and diagrams (e.g., Figure 2) but does not include formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | Our code is available at https://github.com/Trustworthy-ML-Lab/VLG-CBM |
| Open Datasets | Yes | Following prior work [15], we conduct experiments on five image recognition datasets: CIFAR10, CIFAR100[7], CUB[23], Places365[30] and Image Net[18]. |
| Dataset Splits | Yes | We tune the hyperparameters for our method using 10% of the training data as validation for the CIFAR10, CIFAR100, CUB and Image Net datasets. For Places365, we use 5% of the training data as validation. |
| Hardware Specification | Yes | Our experiments run on a server with 10 CPU cores, 64 GB RAM, and 1 Nvidia 2080Ti GPU. |
| Software Dependencies | No | The paper mentions optimizers and models like Adam[5], GLM-SAGA[24], CLIP-RN50, ResNet-18/50. However, it does not provide specific version numbers for underlying software frameworks (e.g., PyTorch, TensorFlow, CUDA) or other key libraries. |
| Experiment Setup | Yes | We tune the CBL with Adam[5] optimizer with learning rate 1e-4 and weight decay 1e-5. ... We set T = 0.15 in Eq. (2) in all our experiments. |