Results 11 to 20 of about 6,314,796 (253)
Contrastive Representation Learning: A Framework and Review
IEEE Access, 2020 Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned ...
Phuc H. Le-Khac +2 more
doaj +3 more sources
Contrastive Learning Using Spectral Methods [PDF]
, 2017 In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another. For example, given a background corpus of news articles together with
Parkes, David +3 more
core +6 more sources
Applied Sciences, 2023 Font is a crucial design aspect, however, classifying fonts is challenging compared with that of other natural objects, as fonts differ from images. This paper presents the application of contrastive learning in font style classification.
Irfanullah Memon +2 more
doaj +1 more source
Clustering of Short Texts Based on Dynamic Adjustment for Contrastive Learning
IEEE Access, 2022 Faced with the large amount of unlabeled short text data appearing on the Internet, it is necessary to categorize them using clustering that can divide text into several clusters based on similarity degree of text semantics.
Ruihui Li, Hongbin Wang
doaj +1 more source
CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection
Applied Sciences, 2023 Anomaly detection has gained increasing attention in recent years, but detecting anomalies in time series data remains challenging due to temporal dynamics, label scarcity, and data diversity in real-world applications.
Huynh Cong Viet Ngu, Keon Myung Lee
doaj +1 more source
An Asymmetric Contrastive Loss for Handling Imbalanced Datasets
Entropy, 2022 Contrastive learning is a representation learning method performed by contrasting a sample to other similar samples so that they are brought closely together, forming clusters in the feature space.
Valentino Vito, Lim Yohanes Stefanus
doaj +1 more source
SelfCCL: Curriculum Contrastive Learning by Transferring Self-Taught Knowledge for Fine-Tuning BERT
Applied Sciences, 2023 BERT, the most popular deep learning language model, has yielded breakthrough results in various NLP tasks. However, the semantic representation space learned by BERT has the property of anisotropy.
Somaiyeh Dehghan, Mehmet Fatih Amasyali
doaj +1 more source
Dual Space Graph Contrastive Learning [PDF]
, 2022 Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain.
Li, L +5 more
core +1 more source
Al-Takhlil al-Taqabuly fi Ta'lim al-Lughah al-'Arabiyyah
Jurnal Al Bayan: Jurnal Jurusan Pendidikan Bahasa Arab, 2020 This article explains systematically the nature of contrastive analysis in language learning. Throughout this article the writer investigates the development of contrastive analysis in the field of language learning, its main objectives, some hypotheses ...
Ahmad Bukhari Muslim
doaj +1 more source
Grouped Contrastive Learning of Self-Supervised Sentence Representation
Applied Sciences, 2023 This paper proposes a method called Grouped Contrastive Learning of self-supervised Sentence Representation (GCLSR), which can learn an effective and meaningful representation of sentences. Previous works maximize the similarity between two vectors to be
Qian Wang +3 more
doaj +1 more source