Privacy Enhanced Multimodal Neural Representations for Emotion Recognition
Authors: Mimansa Jaiswal, Emily Mower Provost7985-7993
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. |
| Researcher Affiliation | Academia | Mimansa Jaiswal, Emily Mower Provost University of Michigan {mimansa, emilykmp}@umich.edu |
| Pseudocode | No | The paper describes the network architecture and training process in text and with a diagram, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | Yes | We use four common emotion recognition datasets: MSPImprov (Busso et al. 2017), MSP-Podcast (Lotfian and Busso 2017), Interactive Emotional Dyadic MOtion Capture (IEMOCAP) dataset (Busso et al. 2008), and Multimodal Stressed Emotion (Mu SE) dataset (Jaiswal et al. 2019). |
| Dataset Splits | Yes | We use validation samples for hyper-parameter selection and early stopping. The hyper-parameters that we use for the main network include: number of convolutional layers {3, 4}, width of the convolutional layers {2, 3}, number of convolutional kernels {32, 64, 128}, number of GRU layers {2, 3}, GRU layers width {32}, number of dense layers {1, 2}, dense layers width {32, 64}, GRL λ {0.3, 0.5, 0.75, 1}. ... We report the average across five-fold speaker-independent crossvalidation in Table 1a and Table 1b. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like GPU/CPU models, memory, or specific computing environments) used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing models using 'the Keras library' but does not specify a version number for Keras or any other software dependencies. |
| Experiment Setup | Yes | The hyper-parameters that we use for the main network include: number of convolutional layers {3, 4}, width of the convolutional layers {2, 3}, number of convolutional kernels {32, 64, 128}, number of GRU layers {2, 3}, GRU layers width {32}, number of dense layers {1, 2}, dense layers width {32, 64}, GRL λ {0.3, 0.5, 0.75, 1}. For the adversarial emotion classification setups, we use the hyper-parameters that maximize the validation emotion classification performance while minimizing the validation gender classification performance. For the attacker s model, we use the following hyper-parameters: number of dense layers {2, 3, 4}, dense layers width {32, 64}. |