Deep Convolutional Sum-Product Networks
Authors: Cory J. Butz, Jhonatan S. Oliveira, André E. dos Santos, André L. Teixeira3248-3255
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our preliminary results on image sampling are encouraging, since the DCSPN sampled images exhibit variability. Experiments on image completion show that DCSPNs significantly outperform competing methods by achieving several state-of-the-art mean squared error (MSE) scores in both left-completion and bottom-completion in benchmark datasets. |
| Researcher Affiliation | Academia | Cory J. Butz butz@cs.uregina.ca University of Regina Canada Jhonatan S. Oliveira oliveira@cs.uregina.ca University of Regina Canada Andr e E. dos Santos dossantos@cs.uregina.ca University of Regina Canada Andr e L. Teixeira teixeira@cs.uregina.ca University of Regina Canada |
| Pseudocode | Yes | Algorithm 1 Mask MPE Backward Propagation |
| Open Source Code | No | The paper does not state that its own source code is publicly available, nor does it provide a link. |
| Open Datasets | Yes | Table 1 gives the mean squared error (MSE) scores for left-completion and bottom-completion in the Olivetti Face dataset (Samaria and Harter 1994). Table 2 shows left-completion and bottom-completion MSE scores in the Caltech datasets (Fei-Fei, Fergus, and Perona 2007). |
| Dataset Splits | No | For each dataset, we randomly set aside one third (up to 50 images) for testing. This specifies a test split but does not mention a separate validation split or explicit training/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like TensorFlow and ADAM but does not provide specific version numbers for these or other key software components. |
| Experiment Setup | Yes | For training, we use ADAM (Kingma and Ba 2014) with a learning rate of 0.005. ... Here, we use the hyperparameter values suggested in (Amos 2016) and 100 epochs during training. ... A convolutional layer follows every representational layer and every sum-pooling layer. All convolutional layers have filter sizes height-by-width matching the layer size. Two sum-pooling layers follow each convolutional layer: one with a window size of 1-by-2 and the other 2-by-1. Alternate the window sizes of 1-by-2 and 2-by-1 with 2-by-2 and 2-by-2 every n layers. This hyperparameter n is tuned per dataset and varied between 70 and 100 in our experiments. |