Contextual RNN-GANs for Abstract Reasoning Diagram Generation
Authors: Viveka Kulharia, Arnab Ghosh, Amitabha Mukerjee, Vinay Namboodiri, Mohit Bansal
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning, where it performs competitively with 10th-grade human performance but there is still scope for interesting improvements as compared to college-grade human performance. We also evaluate our model on a standard video next-frame prediction task, achieving improved performance over comparable state-of-the-art. |
| Researcher Affiliation | Academia | Viveka Kulharia, 1 Arnab Ghosh, 1 Amitabha Mukerjee,1 Vinay Namboodiri,1 Mohit Bansal2 1IIT Kanpur 2UNC Chapel Hill |
| Pseudocode | No | The paper describes the architecture and training process of the Context-RNN-GAN model using mathematical formulations and textual descriptions, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available, nor does it include links to a code repository. |
| Open Datasets | No | We collected the data from several IQ test books and online resources (Aggarwal, 2016), (Sijwali and Sijwali, 2016), (Gupta, 2016) and (Jha and Siddiquee, 2011). |
| Dataset Splits | Yes | The models were validated (tuned) using the first 50% of the answer figure set and tested on the remaining unseen 50% of the answer figure set from the test/validation set of 100 questions. |
| Hardware Specification | No | The paper describes the model architectures and training procedures, but it does not specify any hardware details such as GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions using Adam Optimizer but does not specify software dependencies like programming languages (e.g., Python), deep learning frameworks (e.g., TensorFlow, PyTorch), or their specific version numbers. |
| Experiment Setup | Yes | The best (validated) hyperparameters for the Context-RNN-GAN was a GRU with two layers of 400 hidden units each for the generator and a GRU with a single layer and 500 hidden units for the discriminator... The final models of Context-RNN-GAN and the RNN-GAN models use λadv = 0.05, λp = 1, and they use p = 1 for the DAT-DAR task and p = 2 for the next-frame prediction task... In all of the above models a dropout of 0.50 was applied for each hidden layer. All of the models were trained using the Adam Optimizer (Kingma and Ba, 2014). |