VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
Authors: Divyansh Srivastava, Ge Yan, Lily Weng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {ddivyansh, geyan, lweng}@ucsd.edu 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. |