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Contrastive Learning

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)

Papers

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Showing 6301–6325 of 6661 papers

TitleStatusHype
SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking—0
SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection—0
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning—0
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs—0
Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework—0
sEEG-based Encoding for Sentence Retrieval: A Contrastive Learning Approach to Brain-Language Alignment—0
Seeing Objects in dark with Continual Contrastive Learning—0
Seeing Your Speech Style: A Novel Zero-Shot Identity-Disentanglement Face-based Voice Conversion—0
SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation—0
Visual Self-supervised Learning Scheme for Dense Prediction Tasks on X-ray Images—0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation—0
Segmentation of VHR EO Images using Unsupervised Learning—0
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models—0
Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations—0
Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity—0
Selective Structured State-Spaces for Long-Form Video Understanding—0
Self-Adaptive Reconstruction with Contrastive Learning for Unsupervised Sentence Embeddings—0
Self-Calibrated Dual Contrasting for Annotation-Efficient Bacteria Raman Spectroscopy Clustering and Classification—0
Self-Contrastive Forward-Forward Algorithm—0
Self-Contrastive Learning—0
Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Self-degraded contrastive domain adaptation for industrial fault diagnosis with bi-imbalanced data—0
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification—0
Self-Distilled Representation Learning for Time Series—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified