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..
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition
Authors: Mimansa Jaiswal, Emily Mower Provost7985-7993
AAAI 2020 | Venue PDF | 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 EMAIL |
| 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}. |