Homology Consistency Constrained Efficient Tuning for Vision-Language Models
Authors: Huatian Zhang, Lei Zhang, Yongdong Zhang, Zhendong Mao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on few-shot learning over 11 datasets and domain generalization demonstrate the effectiveness and robustness of our method. |
| Researcher Affiliation | Academia | Huatian Zhang, Lei Zhang, Yongdong Zhang, Zhendong Mao University of Science and Technology of China huatianzhang@mail.ustc.edu.cn, {leizh23,zhyd73,zdmao}@ustc.edu.cn |
| Pseudocode | No | The paper describes methodological steps verbally but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available 2https://github.com/htzhang-code/HC |
| Open Datasets | Yes | We conduct the few-shot learning evaluation on 11 benchmark datasets including Caltech101 [48], DTD [49], Euro SAT [50], FGVCAircraft [51], Flowers102 [52], Food101 [53], Image Net [54], Oxford Pets [55], Stanford Cars [56], SUN397 [57] and UCF101 [58]. |
| Dataset Splits | No | We sample 1, 2, 4, 8 and 16 shots per class, respectively, for model training and evaluate on full test sets. The paper does not explicitly specify a validation set split or its use. |
| Hardware Specification | Yes | All experiments are conducted on a single NVIDIA A40 GPU. |
| Software Dependencies | No | The paper mentions optimizers like 'Adam optimizer' and 'Adam W optimizer' and frameworks like 'CLIP' and 'PyTorch' (implicitly by citing CLIP), but does not provide specific version numbers for critical software components or libraries. |
| Experiment Setup | Yes | The training batch size is 256. We employ the Adam optimizer with an initial learning rate of 1e 4 on Image Net and 1e 3 on others, and the learning rates decay with cosine learning rate schedule following Task Res. ... We set initial learning rate as 1e 3. All experiments are conducted on a single NVIDIA A40 GPU. |