Self-Supervised Learning for Specified Latent Representation

Liu, Chicheng; Song, Lei; Zhang, Jiwen; Chen, Ken; Xu, Jing

doi:10.1109/tfuzz.2019.2904237

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…However, unsupervised learning has no label data at all, which may lead to slow speed and low precision [25]. Self-supervised learning uses the input data to generate supervisory information and benefits almost all types of downstream tasks [26,27]. With Google's successful application of reinforcement learning in the Go game, reinforcement learning has attracted the worldwide attention of researchers.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

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With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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Section: Introductionmentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

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Semi-supervised feature selection based on pairwise constraint-guided dual space latent representation learning and double sparse graphs discriminant

Chen

et al. 2022

Appl Intell

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CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

Zhang

Liu

et al. 2021

IEEE Trans. Fuzzy Syst.

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Sarcasm detection in conversation (SDC), a theoretically and practically challenging artificial intelligence (AI) task, aims to discover elusively ironic, contemptuous and metaphoric information implied in daily conversations. Most of the recent approaches in sarcasm detection have neglected the intrinsic vagueness and uncertainty of human language in emotional expression and understanding. To address this gap, we propose a complex-valued fuzzy network (CFN) by leveraging the mathematical formalisms of quantum theory (QT) and fuzzy logic. In particular, the target utterance to be recognized is considered as a quantum superposition of a set of separate words. The contextual interaction between adjacent utterances is described as the interaction between a quantum system and its surrounding environment, constructing the quantum composite system, where the weight of interaction is determined by a fuzzy membership function. In order to model both the vagueness and uncertainty, the aforementioned superposition and composite systems are mathematically encapsulated in a density matrix. Finally, a quantum fuzzy measurement is performed on the density matrix of each utterance to yield the probabilistic outcomes of sarcasm recognition. Extensive experiments are conducted on the MUStARD and the 2020 sarcasm detection Reddit track datasets, and the results show that our model outperforms a wide range of strong baselines.

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Self-Supervised Learning for Specified Latent Representation

Cited by 4 publications

References 33 publications

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Semi-supervised feature selection based on pairwise constraint-guided dual space latent representation learning and double sparse graphs discriminant

CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

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