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..
Adversarial Learning of Privacy-Preserving and Task-Oriented Representations
Authors: Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, Ming-Hsuan Yang12434-12441
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the proposed method on face attribute prediction, showing that our method allows protecting visual privacy with a small decrease in utility performance. In addition, we show the utilityprivacy trade-off with different choices of hyperparameter for negative perceptual distance loss at training, allowing service providers to determine the right level of privacy-protection with a certain utility performance. Moreover, we provide an extensive study with different selections of features, tasks, and the data to further analyze their influence on privacy protection. |
| Researcher Affiliation | Collaboration | Taihong Xiao,1 Yi-Hsuan Tsai,2 Kihyuk Sohn,2 Manmohan Chandraker,2,3 Ming-Hsuan Yang1 1University of California, Merced 2NEC Laboratories America 3University of California, San Diego |
| Pseudocode | No | The paper includes a conceptual diagram (Figure 2) but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We use the widely-used Celeb A (Liu et al. 2015) and MSCeleb-1M (Guo et al. 2016) datasets for experiments. |
| Dataset Splits | Yes | In most experiments, we split the Celeb A dataset into three parts, X1 with 160k images, X2 with 40k images, and the test set T with the rest. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., specific GPU or CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'Res Net-50 model' but does not provide specific version numbers for software libraries, frameworks, or environments (e.g., PyTorch version, TensorFlow version, Python version). |
| Experiment Setup | Yes | Table 2: Results with different λ2 in the training stage. Other hyperparameters are fixed: λ1 = 1, μ1 = 0, μ2 = 1. We use the Res Net-50 model (He et al. 2016) for feature representation and two fully connected layers for latent classifier f. The Dec uses up-sampling layers to decode features to pixel images. |