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 [1].
Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
Authors: Xinxun Xu, Muli Yang, Yanhua Yang, Hao Wang
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our PDFD over state-of-the-art competitors. and Extensive experiments conducted on two popular large-scale datasets demonstrate that our proposed PDFD significantly outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Xinxun Xu , Muli Yang , Yanhua Yang and Hao Wang Xidian University EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | No explicit statement or link was found indicating the provision of open-source code for the described methodology. |
| Open Datasets | Yes | There are two widely-used large-scale sketch datasets Sketchy [Sangkloy et al., 2016] and TU-Berlin [Eitz et al., 2012] for ZS-SBIR. and VGG16 [Simonyan and Zisserman, 2014] pre-trained on Image Net [Deng et al., 2009] dataset (before the last pooling layer). |
| Dataset Splits | No | The paper defines a training set Dtr = {X seen, Sseen, Yseen} and a test set Dte = {X un, Yun} for the zero-shot setting, but does not explicitly provide percentages or specific counts for training, validation, and test splits for reproducibility. It states 'Following the same zero-shot data partitioning in SEMPCYC [Dutta and Akata, 2019]', but the exact numerical splits are not provided within this paper. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not provide a specific version number. Other mentioned software or models (e.g., Adam, VGG16, Word2Vec, GloVe) do not include version numbers for the specific implementation used. |
| Experiment Setup | Yes | PDFD is trained with Adam [Kingma and Ba, 2014] optimizer on Py Torch with an initial learning rate lr = 0.0001, β1 = 0.5, β2 = 0.99. The input size of the image/sketch is 224 224. We use the grid search method to select the best coefficients, which are λadv = 1.0, λrec = 1.0, λmcls = 1.0, λccls = 0.01 when training on Sketch and λadv = 1.0, λrec = 0.5, λmcls = 0.4, λccls = 0.4 when training on TUBerlin. |