Modular Continual Learning in a Unified Visual Environment
Authors: Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In 3 we describe and evaluate comparative performance of multiple Re Ma P module architectures on a variety of Touch Stream tasks. We compared each architecture across 12 variants of visual SR, MTS, and localization tasks, using fixed visual encoding features from layer FC6 of VGG-16. |
| Researcher Affiliation | Academia | Kevin T. Feigelis Department of Physics Stanford Neurosciences Institute Stanford University Stanford, CA 94305 feigelis@stanford.edu Blue Sheffer Department of Psychology Stanford University Stanford, CA 94305 bsheffer@stanford.edu Daniel L. K. Yamins Departments of Psychology and Computer Science Stanford Neurosciences Institute Stanford University Stanford, CA 94305 yamins@stanford.edu |
| Pseudocode | Yes | Algorithm 1: Re Ma P Reward Map Prediction |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | Although we work with modern large-scale computer vision-style datasets and tasks in this work, e.g. Image Net (Deng et al. (2009)) and MS-COCO (Lin et al. (2014)). |
| Dataset Splits | No | Each class has 1300 unique training instances, and 50 unique validation instances." The paper mentions 'cross-validated fashion' for learning rates but does not provide overall training/validation/test dataset splits for reproducibility beyond the number of validation instances for one dataset. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU model, CPU type, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions algorithms (e.g., ADAM) and network architectures (e.g., VGG-16) and activation functions (e.g., Re LU, CRe LU) but does not specify versions for any key software components or libraries used for implementation. |
| Experiment Setup | Yes | Module weights were initialized using a normal distribution with µ = 0.0, σ = 0.01, and optimized using the ADAM algorithm (Kingma & Ba (2014)) with parameters β1 = 0.9, β2 = 0.999 and ϵ = 1e 8. Learning rates were optimized on a per-task, per-architecture basis in a cross-validated fashion. Values used in the present study may be seen in Table S2. |