Maximal Sparsity with Deep Networks?
Authors: Bo Xin, Yizhou Wang, Wen Gao, David Wipf, Baoyuan Wang
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the proposed system to a practical photometric stereo computer vision problem, where the goal is to estimate the 3D geometry of an object using only 2D photos taken from a single camera under different lighting conditions. In this context, shadows and specularities represent sparse outliers that must be simultaneously removed from 104 106 surface points. We achieve state-of-the-art performance using only weak supervision despite a minuscule computational budget appropriate for real-time mobile environments. Synthetic Tests with Correlated Dictionaries: We generate a dictionary matrix Φ Rn m using Φ = Pn i=1 1 i2 uiv i , where ui Rn and vi Rm have iid elements drawn from N(0, 1). We also rescale each column of Φ to have unit ℓ2 norm. |
| Researcher Affiliation | Collaboration | Bo Xin1,2 Yizhou Wang1 Wen Gao1 Baoyuan Wang3 David Wipf2 1Peking University 2Microsoft Research, Beijing 3Microsoft Research, Redmond |
| Pseudocode | No | The paper describes algorithms using mathematical equations and descriptions but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing code or links to source code repositories for the described methodology. |
| Open Datasets | No | Synthetic Tests with Correlated Dictionaries: We generate a dictionary matrix Φ Rn m using Φ = Pn i=1 1 i2 uiv i , where ui Rn and vi Rm have iid elements drawn from N(0, 1). ... As real-world training data is expensive to acquire, we instead use weak supervision by synthetically generating a training set as follows. |
| Dataset Splits | No | We used N1 = 600000 samples for training and the remaining N2 = 100000 for testing. A specific validation split is not explicitly mentioned. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not list specific software components with version numbers that would be needed for replication. |
| Experiment Setup | No | Detailed network design, optimization setup, and alternative metrics can be found in [26]. |