SOTAVerified

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 3601–3650 of 6661 papers

TitleStatusHype
M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence RetrievalCode0
Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting—0
Unsupervised Audio-Visual Segmentation with Modality Alignment—0
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning—0
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images—0
USE: Dynamic User Modeling with Stateful Sequence ModelsCode0
UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All—0
Automated Contrastive Learning Strategy Search for Time Series—0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
Investigating the Benefits of Projection Head for Representation Learning—0
Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
End-to-end multi-modal product matching in fashion e-commerce—0
Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain AdaptationCode0
RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive LearningCode0
Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive LearningCode0
Exploring Chinese Humor Generation: A Study on Two-Part Allegorical Sayings—0
Probabilistic World Modeling with Asymmetric Distance Measure—0
Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control—0
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained AlignmentCode0
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent PerceptionCode0
Computer User Interface Understanding. A New Dataset and a Learning Framework—0
Multiscale Matching Driven by Cross-Modal Similarity Consistency for Audio-Text Retrieval—0
Improving Medical Multi-modal Contrastive Learning with Expert AnnotationsCode0
Lifelong Person Re-Identification with Backward-Compatibility—0
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization—0
Unsupervised Modality-Transferable Video Highlight Detection with Representation Activation Sequence Learning—0
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality—0
Anatomical Structure-Guided Medical Vision-Language Pre-training—0
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow—0
Deep Submodular Peripteral Networks—0
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?—0
MolBind: Multimodal Alignment of Language, Molecules, and Proteins—0
Intra-video Positive Pairs in Self-Supervised Learning for UltrasoundCode0
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model—0
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing ModelsCode0
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without AnnotationsCode0
Spatiotemporal Representation Learning for Short and Long Medical Image Time SeriesCode0
Self-supervised Contrastive Learning for Implicit Collaborative Filtering—0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation—0
Disentangling Policy from Offline Task Representation Learning via Adversarial Data AugmentationCode0
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning—0
Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)—0
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature AlignmentCode0
Exploiting Style Latent Flows for Generalizing Deepfake Video Detection—0
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning—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