Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
Authors: Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on real-life datasets (images and text), as well as on synthetic data, demonstrates that the proposed approach can considerably outperform the state-of-art non-adaptive online sparse coding of [Mairal et al., 2009] in the presence of non-stationary data. Moreover, we identify certain dataand model properties associated with such improvements. |
| Researcher Affiliation | Collaboration | Sahil Garg , Irina Rish, Guillermo Cecchi, and Aurelie Lozano IBM Thomas J. Watson Research Center sahilgar@usc.edu, {rish, gcecchi, aclozano}@us.ibm.com |
| Pseudocode | Yes | Algorithm 1 Neurogenetic Online Dictionary Learning (NODL) |
| Open Source Code | No | The paper states 'The extended version of this paper is available at arxiv.org/abs/1701.06106,' which refers to the paper itself, not the source code for the methodology. No other statements or links regarding code availability are provided. |
| Open Datasets | Yes | Our first domain includes the images of Oxford buildings, i.e. urban environment 5, while the second uses a combination of images from Flowers 6 and Animals 7 image databases (natural environment). Footnotes provide URLs: 5 www.robots.ox.ac.uk/ vgg/data/oxbuildings/, 6 www.robots.ox.ac.uk/ vgg/data/flowers/102/, 7 www.robots.ox.ac.uk/ vgg/data/pets/. |
| Dataset Splits | Yes | We selected 5700 images for training and another 5700 for testing; each subset contained 1900 images of each type (i.e., Oxford, Flowers, Animals). In the training phase, the algorithms receive a sequence of 1900 samples from the first domain (Oxford), and then a sequence of 3800 samples from the second domain (1900 Flowers and 1900 Animals, permuted randomly); the batch size is 200 images. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming language versions, libraries, or frameworks. |
| Experiment Setup | Yes | Parameter settings. We selected 5700 images for training and another 5700 for testing; each subset contained 1900 images of each type (i.e., Oxford, Flowers, Animals). In the training phase, the algorithms receive a sequence of 1900 samples from the first domain (Oxford), and then a sequence of 3800 samples from the second domain (1900 Flowers and 1900 Animals, permuted randomly); the batch size is 200 images ([Mairal et al., 2009] used a batch of size 256, though image patches rather than full images). We use Pearson correlation threshold γ = 0.9, group sparsity parameter λg = 0.03 and λg = 0.07, for 32x32 and 100x100 images, respectively; ck = 50 is the upper bound on the number of new dictionary elements at each iteration. |