Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
Authors: Bowen Li, Xiaojuan Qi, Philip Torr, Thomas Lukasiewicz
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |