Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings
Authors: Shweta Mahajan, Iryna Gurevych, Stefan Roth
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our model on diverse tasks, including image captioning and text-to-image synthesis. To that end, we perform experiments on the COCO dataset (Lin et al., 2014). |
| Researcher Affiliation | Academia | Shweta Mahajan, Iryna Gurevych, Stefan Roth Department of Computer Science, TU Darmstadt, Germany |
| Pseudocode | No | The paper provides architectural diagrams and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or providing links to a code repository. |
| Open Datasets | Yes | To that end, we perform experiments on the COCO dataset (Lin et al., 2014). |
| Dataset Splits | Yes | It contains 82,783 training and 40,504 validation images, each with five captions. Following Wang et al. (2016); Mao et al. (2015) for image captioning, we use 118,287 data points for training and evaluate on 1,000 test images. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like VGG-16, GRU, and LSTM in the architecture details but does not provide specific version numbers for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The overall objective of our semi-supervised generative model framework to be minimized is given by Lµ(xt,xv) = λ1DKL(qθ1 zs|xt, xv) pφs(zs) + λ2DKL(qθ2 z t|xt, zs) pφt(z t|zs) + λ3DKL qθ3(z v|xv, zs) pφv(z v|zs) + λ4Lrec t (xt, xt) + λ5Lrec v (xv, xv). |