Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Authors: Michael Gygli, Mohammad Norouzi, Anelia Angelova
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed Deep Value Networks on 3 tasks: multi-label classification, binary image segmentation, and a 3-class face segmentation task. Section 5.4 investigates the sampling mechanisms for DVN training, and Section 5.5 visualizes the learned models. |
| Researcher Affiliation | Collaboration | Michael Gygli 1 * Mohammad Norouzi 2 Anelia Angelova 2 ... 1ETH Z urich & gifs.com 2Google Brain, Mountain View, USA. Correspondence to: Michael Gygli <gygli@vision.ee.ethz.ch>, Mohammad Norouzi <mnorouzi@google.com>. |
| Pseudocode | Yes | Algorithm 1 Deep Value Network training |
| Open Source Code | Yes | Our source code based on Tensor Flow (Abadi et al., 2015) is available at https://github.com/gyglim/dvn. |
| Open Datasets | Yes | We use standard benchmarks in multi-label classification, namely Bibtex and Bookmarks, introduced in (Katakis et al., 2008). |
| Dataset Splits | Yes | We tune the hyperparameters of the model on a validation set and, once best hyper-parameters are found, fine-tune on the combination of training and validation sets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow (Abadi et al., 2015)' as the basis for the source code, but does not specify a version number for TensorFlow or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | We use a learning rate of 0.01 and apply dropout on the first fully connected layer with the keeping probability 0.75 as determined on the validation set. |