Feb 3, 2023 · The proposed techniques give rise to a semi-supervised learning framework based on bi-level optimization, achieving new state-of-the-art results ...
Jun 4, 2024 · Contrastive learning demonstrates great promise for representation learning. Data augmentations play a critical role in contrastive learning ...
The proposed Contrastive Learning with Consistent Representations (CoCor) improves contrastive learning performance by exploring diverse data augmentations ...
We build upon these ideas by leveraging con- trastive learning on the network's explanation heatmaps in- stead of the representations. This aligns with our ...
Feb 3, 2023 · Contrastive learning demonstrates great promise for representation learning. Data augmentations play a critical role in contrastive learning ...
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Mar 6, 2024 · Experimental results on three public AU datasets demonstrate that the learned AU representation is discriminative for AU detection. Our method ...
Contrastive learning has actively been used for unsupervised graph representation learning owing to its success in computer vision. Most graph contrastive ...
SCRL is a self-supervised learning method that allows you to obtain a spatially consistent dense representation, especially useful for localization tasks ...
Sep 29, 2023 · This approach employs adversarial domain adaptation to disentangle features from distinct modalities, fostering a shared representation.
Abstract. Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones.