Few-shot Generation via Recalling Brain-Inspired Episodic-Semantic Memory
Authors: Zhibin Duan, Zhiyi Lv, Chaojie Wang, Bo Chen, Bo An, Mingyuan Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments |
| Researcher Affiliation | Academia | National Laboratory of Radar Signal Processing Xidian University, Xi an, Shaanxi 710071, China xd_zhibin@163.com, xd_silly@163.com, bchen@mail.xidian.edu.cn Bo An Nanyang Technological University Mingyuan Zhou The University of Texas at Austin |
| Pseudocode | Yes | Algorithm 1 Semantic Memory recall and update algorithm |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | The experiments are conducted on five widely used benchmark datasets of various sizes, including binarized Omniglot [2], MNIST [35], DOUBLE-MNIST [36], Celeb A [37] and FS-CIFAR100 [38]. |
| Dataset Splits | No | The split of training/testing set (also known as background-evaluation split) follows Lake et al. [2]... The paper mentions training and testing sets but does not explicitly provide details about a separate validation set split or its proportion. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud computing instances). |
| Software Dependencies | No | The paper mentions various models and frameworks (e.g., VAE, Diffusion model, DDPM), but it does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | Model Settings: The details about model settings can be found in Appendix B. Memory size Across all datasets, we set the size of each semantic memory block (category) in the VSM-based model to 1, and each episodic memory block to 5. For models that exclusively depend on episodic memory (CNS + Episodic, SCHA + Episodic, and Episodic-diffusion), we establish a memory size of 10000, with the episodic memory functioning as a first-in, first-out queue. |