An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions

• URL: http://arxiv.org/abs/2103.03934v1
• Date: Fri, 5 Mar 2021 20:40:52 GMT
• Title: An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions
• Authors: Henrique Siqueira, Pablo Barros, Sven Magg and Stefan Wermter
• Abstract summary: Semi-supervised learning through ensemble predictions is an efficient strategy to leverage the high exposure of unlabelled facial expressions during human-robot interactions. Traditional ensemble-based systems are composed of several independent classifiers leading to a high degree of redundancy. We show that our approach is able to continually learn facial expressions through ensemble predictions using unlabelled samples from different data distributions.
• Score: 19.72032908764253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social robots able to continually learn facial expressions could progressively improve their emotion recognition capability towards people interacting with them. Semi-supervised learning through ensemble predictions is an efficient strategy to leverage the high exposure of unlabelled facial expressions during human-robot interactions. Traditional ensemble-based systems, however, are composed of several independent classifiers leading to a high degree of redundancy, and unnecessary allocation of computational resources. In this paper, we proposed an ensemble based on convolutional networks where the early layers are strong low-level feature extractors, and their representations shared with an ensemble of convolutional branches. This results in a significant drop in redundancy of low-level features processing. Training in a semi-supervised setting, we show that our approach is able to continually learn facial expressions through ensemble predictions using unlabelled samples from different data distributions.

Related papers

• A distributional simplicity bias in the learning dynamics of transformers [50.91742043564049]
  We show that transformers, trained on natural language data, also display a simplicity bias. Specifically, they sequentially learn many-body interactions among input tokens, reaching a saturation point in the prediction error for low-degree interactions. This approach opens up the possibilities of studying how interactions of different orders in the data affect learning, in natural language processing and beyond.
  arXiv  Detail & Related papers  (2024-10-25T15:39:34Z)
• Dynamic Post-Hoc Neural Ensemblers [55.15643209328513]
  In this study, we explore employing neural networks as ensemble methods. Motivated by the risk of learning low-diversity ensembles, we propose regularizing the model by randomly dropping base model predictions. We demonstrate this approach lower bounds the diversity within the ensemble, reducing overfitting and improving generalization capabilities.
  arXiv  Detail & Related papers  (2024-10-06T15:25:39Z)
• Neural Clustering based Visual Representation Learning [61.72646814537163]
  Clustering is one of the most classic approaches in machine learning and data analysis. We propose feature extraction with clustering (FEC), which views feature extraction as a process of selecting representatives from data. FEC alternates between grouping pixels into individual clusters to abstract representatives and updating the deep features of pixels with current representatives.
  arXiv  Detail & Related papers  (2024-03-26T06:04:50Z)
• Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings [19.667133565610087]
  Compositional generalization is easy for humans but hard for deep neural networks. We propose to improve this ability by using iterated learning on models with simplicial embeddings. We show that this combination of changes improves compositional generalization over other approaches.
  arXiv  Detail & Related papers  (2023-10-28T18:30:30Z)
• Self-Supervised Visual Representation Learning with Semantic Grouping [50.14703605659837]
  We tackle the problem of learning visual representations from unlabeled scene-centric data. We propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning.
  arXiv  Detail & Related papers  (2022-05-30T17:50:59Z)
• Learning from Heterogeneous Data Based on Social Interactions over Graphs [58.34060409467834]
  This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting.
  arXiv  Detail & Related papers  (2021-12-17T12:47:18Z)
• Information Maximization Clustering via Multi-View Self-Labelling [9.947717243638289]
  We propose a novel single-phase clustering method that simultaneously learns meaningful representations and assigns the corresponding annotations. This is achieved by integrating a discrete representation into the self-supervised paradigm through a net. Our empirical results show that the proposed framework outperforms state-of-the-art techniques with the average accuracy of 89.1% and 49.0%, respectively.
  arXiv  Detail & Related papers  (2021-03-12T16:04:41Z)
• Network Classifiers Based on Social Learning [71.86764107527812]
  We propose a new way of combining independently trained classifiers over space and time. The proposed architecture is able to improve prediction performance over time with unlabeled data. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers.
  arXiv  Detail & Related papers  (2020-10-23T11:18:20Z)
• Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks [20.09586211332088]
  We present experiments on Ensembles with Shared Representations based on convolutional networks. We show that redundancy and computational load can be dramatically reduced by varying the branching level of the ESR. Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error.
  arXiv  Detail & Related papers  (2020-01-17T14:32:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.