2,049 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
In this paper, we propose a new unsupervised domain adaptation (DA) method
called layer-adapted implicit distribution alignment networks (LIDAN) to
address the challenge of cross-corpus speech emotion recognition (SER). LIDAN
extends our previous ICASSP work, deep implicit distribution alignment networks
(DIDAN), whose key contribution lies in the introduction of a novel
regularization term called implicit distribution alignment (IDA). This term
allows DIDAN trained on source (training) speech samples to remain applicable
to predicting emotion labels for target (testing) speech samples, regardless of
corpus variance in cross-corpus SER. To further enhance this method, we extend
IDA to layer-adapted IDA (LIDA), resulting in LIDAN. This layer-adpated
extention consists of three modified IDA terms that consider emotion labels at
different levels of granularity. These terms are strategically arranged within
different fully connected layers in LIDAN, aligning with the increasing
emotion-discriminative abilities with respect to the layer depth. This
arrangement enables LIDAN to more effectively learn emotion-discriminative and
corpus-invariant features for SER across various corpora compared to DIDAN. It
is also worthy to mention that unlike most existing methods that rely on
estimating statistical moments to describe pre-assumed explicit distributions,
both IDA and LIDA take a different approach. They utilize an idea of target
sample reconstruction to directly bridge the feature distribution gap without
making assumptions about their distribution type. As a result, DIDAN and LIDAN
can be viewed as implicit cross-corpus SER methods. To evaluate LIDAN, we
conducted extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA
corpora. The experimental results demonstrate that LIDAN surpasses recent
state-of-the-art explicit unsupervised DA methods in tackling cross-corpus SER
tasks
Improved Deep Convolutional Neural Network with Age Augmentation for Facial Emotion Recognition in Social Companion Robotics
Facial emotion recognition (FER) is a critical component for affective computing in social companion robotics. Current FER datasets are not sufficiently age-diversified as they are predominantly adults excluding seniors above fifty years of age which is the target group in long-term care facilities. Data collection from this age group is more challenging due to their privacy concerns and also restrictions under pandemic situations such as COVID-19. We address this issue by using age augmentation which could act as a regularizer and reduce the overfitting of the classifier as well. Our comprehensive experiments show that improving a typical Deep Convolutional Neural Network (CNN) architecture with facial age augmentation improves both the accuracy and standard deviation of the classifier when predicting emotions of diverse age groups including seniors. The proposed framework is a promising step towards improving a participant’s experience and interactions with social companion robots with affective computing
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Understanding predictions made by deep neural networks is notoriously
difficult, but also crucial to their dissemination. As all ML-based methods,
they are as good as their training data, and can also capture unwanted biases.
While there are tools that can help understand whether such biases exist, they
do not distinguish between correlation and causation, and might be ill-suited
for text-based models and for reasoning about high level language concepts. A
key problem of estimating the causal effect of a concept of interest on a given
model is that this estimation requires the generation of counterfactual
examples, which is challenging with existing generation technology. To bridge
that gap, we propose CausaLM, a framework for producing causal model
explanations using counterfactual language representation models. Our approach
is based on fine-tuning of deep contextualized embedding models with auxiliary
adversarial tasks derived from the causal graph of the problem. Concretely, we
show that by carefully choosing auxiliary adversarial pre-training tasks,
language representation models such as BERT can effectively learn a
counterfactual representation for a given concept of interest, and be used to
estimate its true causal effect on model performance. A byproduct of our method
is a language representation model that is unaffected by the tested concept,
which can be useful in mitigating unwanted bias ingrained in the data.Comment: Our code and data are available at:
https://amirfeder.github.io/CausaLM/ Under review for the Computational
Linguistics journa
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