Efficient Generation of Structured Objects with Constrained Adversarial Networks
Authors: Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive empirical analysis shows that CANs efficiently generate valid structures that are both high-quality and novel. We implemented CANs6 using Tensorflow and used Py SDD7 to perform knowledge compilation. We tested CANs using different generator architectures on three real-world structured generative tasks.8 In all cases, we evaluated the objects generated by CANs and those of the baselines using three metrics (adopted from [36]): validity is the proportion of sampled objects that are valid; novelty is the proportion of valid sampled objects that are not present in the training data; and uniqueness is the proportion of valid unique (non-repeated) sampled objects. |
| Researcher Affiliation | Academia | Luca Di Liello University of Trento Pierfrancesco Ardino University of Trento Jacopo Gobbi University of Trento Paolo Morettin KU Leuven Stefano Teso University of Trento firstname.lastname@unitn.it Andrea Passerini University of Trento |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The code is freely available at https://github.com/unitn-sml/CAN |
| Open Datasets | Yes | The structured objects are 14 28 tile-based representations of SMB levels (e.g. Fig. 2) and the training data is obtained by sliding a 28 tiles window over levels from the Video game level corpus [42]. |
| Dataset Splits | No | The paper mentions 'validation experiments' for hyperparameter tuning but does not provide specific numerical details for a train/validation/test dataset split (e.g., percentages or sample counts). |
| Hardware Specification | Yes | In this experiment, we train the model on a NVIDIA RTX 2080 Ti. |
| Software Dependencies | No | The paper mentions 'Tensorflow' and 'Py SDD' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We run all the experiments on a machine with a single 1080Ti GPU for 4 times with random seeds. We address both issues by introducing the SL after an initial bootstrap phase (of 5, 000 epochs)... and by linearly increasing its weight from zero to λ = 0.2... All experiments were run for 12, 000 epochs. Each training run lasted 15000 epochs with all the default hyper parameters defined in [41], and the SL was activated from epoch 5000 with λ = 0.01... The training is stopped once the uniqueness drops under 0.2. |