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 2551–2600 of 6661 papers

TitleStatusHype
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift—0
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition—0
Causal Contrastive Learning for Counterfactual Regression Over Time—0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking—0
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations—0
ICLEA: Interactive Contrastive Learning for Self-supervised Entity Alignment—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data—0
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture—0
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?—0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
How does Contrastive Pre-training Connect Disparate Domains?—0
ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations—0
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
ConVerSum: A Contrastive Learning-based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents—0
Adversarial Masking Contrastive Learning for vein recognition—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Conversation Disentanglement with Bi-Level Contrastive Learning—0
Convergence of energy-based learning in linear resistive networks—0
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling—0
Convergence of End-to-End Training in Deep Unsupervised Contrastive Learning—0
A Rate-Distortion Approach to Domain Generalization—0
Homophily-aware Heterogeneous Graph Contrastive Learning—0
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation—0
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
Adversarial Robustness of Link Sign Prediction in Signed Graphs—0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning—0
Homography augumented momentum constrastive learning for SAR image retrieval—0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning—0
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning—0
Controllable Augmentations for Video Representation Learning—0
Control Image Captioning Spatially and Temporally—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Control False Negative Instances In Contrastive Learning To ImproveLong-tailed Item Categorization—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition—0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
A Probabilistic Interpretation of Transformers—0
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition—0
Hodge-Aware Contrastive Learning—0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds—0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—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