Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
Authors: Nihal V. Nayak, Peilin Yu, Stephen Bach
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTAL EVALUATION In this section, we describe our experiments with CSP . We compare CSP to CLIP-based baselines in the closed-world and open-world settings of compositional zero-shot learning. |
| Researcher Affiliation | Academia | Nihal V. Nayak , Peilin Yu , Stephen H. Bach Department of Computer Science Brown University Providence, RI 02906, USA {nnayak2, pyu12, sbach}@cs.brown.edu |
| Pseudocode | Yes | F PSEUDOCODE Figure 6 shows the Torch-like pseudocode for inference with CSP. |
| Open Source Code | Yes | The code is available at https://github.com/Bats Research/csp. |
| Open Datasets | Yes | We experiment with three attribute-object composition benchmarks: MIT-states (Isola et al., 2015), UT-Zappos (Yu & Grauman, 2014), and C-GQA (Naeem et al., 2021). |
| Dataset Splits | Yes | Table 1: Summary statistics of the datasets used in our experiments. |
| Hardware Specification | Yes | We use a single NVIDIA RTX 3090 or V100 GPU depending on their availability to train all our models. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other key software dependencies with their versions. |
| Experiment Setup | Yes | We train CSP and Co Op by minimizing the cross entropy loss with the Adam optimizer over the seen split in the dataset for 20 epochs. |