Eigen-Distortions of Hierarchical Representations
Authors: Alexander Berardino, Valero Laparra, Johannes Ballé, Eero Simoncelli
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans. ... We use this method to test the ability of a variety of representations to mimic human perceptual sensitivity. |
| Researcher Affiliation | Collaboration | Alexander Berardino Center for Neural Science New York University agb313@nyu.edu Johannes Ballé Center for Neural Science New York University johannes.balle@nyu.edu Valero Laparra Image Processing Laboratory Universitat de València valero.laparra@uv.es Eero Simoncelli Howard Hughes Medical Institute, Center for Neural Science and Courant Institute of Mathematical Sciences New York University eero.simoncelli@nyu.edu |
| Pseudocode | No | The paper describes the power iteration method verbally with equations, but it does not contain a structured pseudocode or algorithm block labeled as such. |
| Open Source Code | No | The paper provides a link to example images and additional examples, but it does not include an unambiguous statement or direct link for the source code of the described methodology. |
| Open Datasets | Yes | For each model under consideration, we synthesized extremal eigen-distortions for 6 images from the Kodak image set2. Downloaded from http://www.cipr.rpi.edu/resource/stills/kodak.html. We trained all of the models on the TID-2008 database, which contains a large set of original and distorted images, along with corresponding human ratings of perceived distortion [Ponomarenko et al., 2009]. |
| Dataset Splits | No | The paper states 'evaluated each model s predictive performance using traditional cross-validation methods on a held-out test set of the TID-2008 database', but it does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology). |
| 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 the use of the Adam algorithm but does not provide specific software details like library names with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions the use of specific optimization algorithms (non-negative least squares, Adam) but does not provide specific experimental setup details such as concrete hyperparameter values or comprehensive training configurations. |