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Found 8 papers, 0 papers with code
Triple Disentangled Representation Learning for Multimodal Affective Analysis
We revisit the disentanglement issue, and propose a novel triple disentanglement approach, TriDiRA, which disentangles the modality-invariant, effective modality-specific and ineffective modality-specific representations from input data.
Learning Subjective Time-Series Data via Utopia Label Distribution Approximation
In this work, we propose Utopia Label Distribution Approximation (ULDA) for time-series data, which makes the training label distribution closer to real-world but unknown (utopia) label distribution.
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition
We find the data distribution in the IEMOCAP dataset is very imbalanced, which may harm models to learn a better representation.
Tripartite: Tackle Noisy Labels by a More Precise Partition
Instead, we propose a Tripartite solution to partition training data more precisely into three subsets: hard, noisy, and clean.
Unsupervised Outlier Detection using Memory and Contrastive Learning
Instead, we think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers.
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition
In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care.
Happy Travelers Take Big Pictures: A Psychological Study with Machine Learning and Big Data
In psychology, theory-driven researches are usually conducted with extensive laboratory experiments, yet rarely tested or disproved with big data.
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem.
