... learning network and cluster centers simultaneously. Despite its success, one limitation of existing models is that the view representations are learned with no consideration of achieving a consistent ... contrastive learning has become ...

... contrastive learning loss; Answer-rationale CL: answer-rationale contrastive learning loss. Method Base (correct answer) Base (predict answer) Base + Joint L Base + Joint ... Consistent Representation Learning for VCR 159 5 Conclusion.

... contrastive learning framework follows the common graph contrastive learning paradigm, and the model is designed to find the consistent representations between different views [18,19]. More precise, each input graph data ( i.e., the ...

... representations from raw behavior data, following previous works [7,20,21]. To make the representations of the target behavior and the ... Consistency 265 3.3 Representation Learning 3.4 Behavior Consistency Contrastive Learning.

... contrastive loss and use antenna-instance loss as a finer-grained loss, enabling the model to learn finer feature representations ... consistent representation at different SNRs. During the contrastive learning phase, the encoder ...

... Contrastive. Learning. Fig. 1. Disentangled contrastive learning for robust textual representations. In this section ... consistent momentum representation to explicitly guarantee feature alignment [8]. The two networks are defined ...

... contrastive module Sect.3.3 learns similar outputs from different views based on clustered soft labels from the original data, which in turn will help the model to develop fine ... Consistent Representation Learning 3 Proposed Method.

... representations into the entity consistency learning module to capture the entity inconsistencies present between diverse modalities . ( c ) Emotional Consistency ... contrastive. 226 L. Wang et al . 3.2 Cross-Modal Contrastive Learning.

... contrastive learning based, and clustering based. Pretext Tasks Based. Approaches based on pretext paradigm first ... consistent representations of negative samples. Thus, MoCo can achieve superior performance without a large batch ...

... consistent representations . Zhang et al . [ 35 ] proposed to capture inter - modality correspondences via multiple contrastive losses . Inspired by their work , we maximize the mutual information between the corresponding pairs through ...