Non-parametric Structured Output Networks
Authors: Andreas Lehrmann, Leonid Sigal
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate non-parametric structured output networks at both the model (DNN+NGM) and the inference level (RIN). Model validation consists of a comparison to baselines along two binary axes, structuredness and non-parametricity. Inference validation compares our RIN unit to the two predominant groups of approaches for inference in structured non-parametric densities, i.e., sampling-based and variational inference (Section 1.1.2). [...] Table 2: Quantitative Evaluation. |
| Researcher Affiliation | Industry | Andreas M. Lehrmann Disney Research Pittsburgh, PA 15213 andreas.lehrmann@disneyresearch.com Leonid Sigal Disney Research Pittsburgh, PA 15213 lsigal@disneyresearch.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We test our approach on simple natural pixel statistics from Microsoft COCO [11] by sampling stripes y = (yi)n i=1 2 [0, 255]n of n = 10 pixels. [11] Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., Dollar, P.: Microsoft COCO: Common Objects in Context. In ar Xiv:1405.0312 [cs.CV]. (2014) |
| Dataset Splits | No | Using this noise process, we generate training and test sets of sizes 100,000 and 1,000, respectively. The paper does not explicitly specify a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We train the other 4 models for 40 epochs using a Gaussian kernel and a diagonal bandwidth matrix for the non-parametric models. The DNN consists of 2 fully-connected layers with 256 units and the kernel weights are constrained to lie on a simplex with a softmax layer. The NGM uses a chain-structured graph that connects each pixel to its immediate neighbors. |