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..
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
Authors: Bowen Li, Xiaojuan Qi, Philip Torr, Thomas Lukasiewicz
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on the CUB bird [27] and more complicated COCO [17] datasets, comparing with the current state of the art, Mani GAN [15], which also focuses on text-guided image manipulation. Results for the method are reproduced using the code released by the authors. Table 1: Quantitative comparison: Fréchet inception distance (FID), accuracy, and realism of the state of the art and our method on CUB and COCO. |
| Researcher Affiliation | Academia | 1University of Oxford, 2University of Hong Kong |
| Pseudocode | No | The paper contains architectural diagrams (Figure 2) but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code will be available at https://github.com/mrlibw/Lightweight-Manipulation. |
| Open Datasets | Yes | The CUB bird [27] dataset contains 8,855 training images and 2,933 test images... COCO [17] contains 82,783 training images and 40,504 validation images... |
| Dataset Splits | Yes | COCO [17] contains 82,783 training images and 40,504 validation images |
| Hardware Specification | Yes | All methods are benchmarked on a single Quadro RTX 6000 GPU. |
| Software Dependencies | No | The paper mentions software components and optimizers like Inception-v3, VGG-16, bidirectional RNN, and Adam optimiser, but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The scale of the output images is 256 x 256, but the size is adjustable to satisfy users preferences. Similarly to [15], there is a trade-off between the generation of new attributes matching the text description and the preservation of text-irrelevant contents of the original image. Therefore, based on the manipulative precision (MP) [15], the whole model is trained 100 epochs on CUB and 10 epochs on COCO using the Adam optimiser [12] with learning rate 0.0002. The hyperparameters λ1, λ2, λ3, and λ4 are all set to 1 for both datasets. |