Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
End-to-end Training for Text-to-Image Synthesis using Dual-Text Embeddings
Authors: Yeruru Asrar Ahmed, Anurag Mittal
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive set of experiments on three text-to-image benchmark datasets (Oxford-102, Caltech-UCSD, and MS-COCO) reveal that having two separate embeddings gives better results than using a shared one and that such an approach performs favourably in comparison with methods that use text representations from a pre-trained text encoder trained using a discriminative approach. Furthermore, we observe that employing separate embeddings gives superior results compared to a shared embedding approach as verified in Section 4.3.4. |
| Researcher Affiliation | Academia | Yeruru Asrar Ahmed EMAIL Department of Computer Science and Engineering Indian Institute of Technology Madras Anurag Mittal EMAIL Department of Computer Science and Engineering Indian Institute of Technology Madras |
| Pseudocode | No | The paper describes the model architecture and methodology in detail across sections 3 and 3.1-3.4, including mathematical formulations of losses and components, but does not present a distinct, structured pseudocode block or algorithm. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository or mention code in supplementary materials for the methodology described. |
| Open Datasets | Yes | DTE-GAN is evaluated on three datasets, namely, 1) Caltech-UCSD birds (CUB) (Welinder et al., 2010), 2) Oxford-102 flowers (Nilsback & Zisserman, 2008), and 3) MS COCO (Lin et al., 2014b) datasets. |
| Dataset Splits | Yes | For CUB and Oxford-102 datasets, we have similar setup to Stack GAN (Zhang et al., 2017b). Ten captions are provided for each image in both the datasets (Reed et al., 2016a). The MS-COCO dataset consists of around 80k training and 40k validation images; and for every image, there are 5 captions provided with the dataset. |
| Hardware Specification | Yes | Due to computational constraints, our model is trained on a single NVIDIA 1080Ti GPU with 12 GB of memory... The model is trained for 600 epochs on CUB and Oxford-102 datasets (takes 4 days in 2 NVIDIA 1080Ti GPUs) and 120 epochs for COCO dataset (takes 7 days in 2 NVIDIA 1080Ti GPUs). |
| Software Dependencies | No | Implementation of the models is done using the Py Torch framework (Paszke et al., 2019) and optimising the network using Adam optimiser (Kingma & Ba, 2015) with the following hyper parameters: β1 = 0.5, β2 = 0.999, batch size = 24, learning rate = 0.0002, λ1 = 1, λ2 = 1 and λ3 = 1. While PyTorch and Adam optimizer are mentioned with citations, specific version numbers for these software components are not provided (e.g., 'PyTorch 1.9' or 'Adam optimizer version X'). |
| Experiment Setup | Yes | Implementation of the models is done using the Py Torch framework (Paszke et al., 2019) and optimising the network using Adam optimiser (Kingma & Ba, 2015) with the following hyper parameters: β1 = 0.5, β2 = 0.999, batch size = 24, learning rate = 0.0002, λ1 = 1, λ2 = 1 and λ3 = 1. Spectral Normalisation (Miyato et al., 2018) is used for all convolutions and fully connected layers in generator and discriminator. The model is trained for 600 epochs on CUB and Oxford-102 datasets... and 120 epochs for COCO dataset. |