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 626–650 of 6661 papers

TitleStatusHype
Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques—0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings—0
Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer—0
Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?—0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science—0
Assisting Mathematical Formalization with A Learning-based Premise RetrieverCode1
SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection—0
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification—0
Unified 3D MRI Representations via Sequence-Invariant Contrastive LearningCode0
Score Combining for Contrastive OOD Detection—0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
Panoramic Interests: Stylistic-Content Aware Personalized Headline GenerationCode0
Fact-Preserved Personalized News Headline GenerationCode0
Disentangled Modeling of Preferences and Social Influence for Group RecommendationCode0
Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning—0
Avoiding Shortcuts: Enhancing Channel-Robust Specific Emitter Identification via Single-Source Domain GeneralizationCode2
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive FrameworkCode0
MedFILIP: Medical Fine-grained Language-Image Pre-trainingCode1
LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight DetectionCode1
ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning—0
AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified RepresentationsCode1
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive LearningCode0
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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