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 4051–4100 of 6661 papers

TitleStatusHype
Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image SegmentationCode1
Implicit Surface Contrastive Clustering for LiDAR Point Clouds—0
HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
Modeling Video As Stochastic Processes for Fine-Grained Video Representation LearningCode1
You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement—0
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic SegmentationCode1
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation—0
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval—0
Sparsely Annotated Semantic Segmentation With Adaptive Gaussian MixturesCode1
PointVST: Self-Supervised Pre-training for 3D Point Clouds via View-Specific Point-to-Image TranslationCode1
Deep Temporal Contrastive Clustering—0
TempCLR: Temporal Alignment Representation with Contrastive LearningCode1
Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative SamplesCode1
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos—0
Precise Location Matching Improves Dense Contrastive Learning in Digital PathologyCode0
Understanding and Improving the Role of Projection Head in Self-Supervised Learning—0
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning—0
Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge BaseCode0
Multi-modal Molecule Structure-text Model for Text-based Retrieval and EditingCode2
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning—0
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval—0
ALCAP: Alignment-Augmented Music CaptionerCode0
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised LearningCode1
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive LearningCode1
Contrastive Learning Reduces Hallucination in ConversationsCode1
WACO: Word-Aligned Contrastive Learning for Speech TranslationCode0
Query-as-context Pre-training for Dense Passage RetrievalCode1
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document UnderstandingCode0
Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications—0
On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation LearningCode1
Hyperbolic Hierarchical Contrastive Hashing—0
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective—0
Attentive Mask CLIPCode1
MAViL: Masked Audio-Video LearnersCode1
NeRF-Art: Text-Driven Neural Radiance Fields StylizationCode1
CLIPPO: Image-and-Language Understanding from Pixels Only—0
Establishing a stronger baseline for lightweight contrastive modelsCode0
MA-GCL: Model Augmentation Tricks for Graph Contrastive LearningCode1
Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders—0
Understanding Zero-Shot Adversarial Robustness for Large-Scale ModelsCode1
Mitigating Negative Style Transfer in Hybrid Dialogue SystemCode0
Tailoring Visual Object Representations to Human Requirements: A Case Study with a Recycling RobotCode0
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits—0
A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps—0
Boosting Semi-Supervised Learning with Contrastive Complementary Labeling—0
On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive LearningCode1
Coarse-to-Fine Contrastive Learning on Graphs—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