Contrastive Learning
2196 papers with code • 1 benchmarks • 11 datasets
Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.
It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.
(Image credit: Schroff et al. 2015)
Benchmarks
These leaderboards are used to track progress in Contrastive Learning
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Libraries
Use these libraries to find Contrastive Learning models and implementationsDatasets
ImageNet-1K
FSD50K
FIVR-200K
US-4
CommitBART
Extended Agriculture-Vision
Latest papers
UniSAR: Modeling User Transition Behaviors between Search and Recommendation
In this paper, we propose a framework named UniSAR that effectively models the different types of fine-grained behavior transitions for providing users a Unified Search And Recommendation service.
WB LUTs: Contrastive Learning for White Balancing Lookup Tables
Automatic white balancing (AWB), one of the first steps in an integrated signal processing (ISP) pipeline, aims to correct the color cast induced by the scene illuminant.
An Experimental Comparison Of Multi-view Self-supervised Methods For Music Tagging
deezer/multi-view-ssl-benchmark
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In this study, we expand the scope of pretext tasks applied to music by investigating and comparing the performance of new self-supervised methods for music tagging.
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discovery
xjtuyw/pnp
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To counteract this inefficiency, we opt to cluster only the unlabelled instances and subsequently expand the cluster prototypes with our introduced potential prototypes to fast explore novel classes.
Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
marqo-ai/gcl
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Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations.
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification
Recent advancements in deep learning have proven highly effective in medical image classification, notably within histopathology.
Latent Guard: a Safety Framework for Text-to-image Generation
Hence, we propose Latent Guard, a framework designed to improve safety measures in text-to-image generation.
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
Our framework leverages both labeled and unlabelled data to robustly learn action representations in videos, combining pseudo-labeling with contrastive learning for effective learning from both types of samples.
End-to-end training of Multimodal Model and ranking Model
In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption.
