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Decoupled Contrastive Learning
Contrastive learning is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented views of the same image as... -
Multimodal Contrastive Learning
The dataset used in the paper is a collection of pairs of observations (xi, ˜xi) from two modalities, where xi ∈ Rd1 and ˜xi ∈ Rd2. The dataset is used to evaluate the... -
Adaptive Multi-head Contrastive Learning
Adaptive Multi-head Contrastive Learning (AMCL) framework, which tackles intra- and inter-sample similarity and adaptive temperature mechanism re-weighting each similarity pair. -
Contrastive learning for compact single image dehazing
Contrastive learning for compact single image dehazing -
Contrastive multiview coding
Contrastive multiview coding. -
A simple framework for contrastive learning of visual representations
A simple framework for contrastive learning of visual representations. -
Prototypical Alignment, Uniformity and Correlation
Contrastive self-supervised learning (CSL) with a prototypical regularization has been introduced in learning meaningful representations for downstream tasks that require strong... -
RECLIP: Resource-efficient CLIP by Training with Small Images
A simple method that minimizes computational resource footprint for CLIP (Contrastive Language Image Pretraining). -
CLIP dataset
The CLIP dataset is used to train a contrastive learning model.
