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 3101–3150 of 6661 papers

TitleStatusHype
A dual contrastive framework—0
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
Contrastive Learning from Synthetic Audio Doppelgängers—0
Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching—0
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Improving Continual Relation Extraction through Prototypical Contrastive Learning—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Contrastive Learning from Pairwise Measurements—0
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval—0
GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections—0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images—0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation—0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning—0
Improving Deep Embedded Clustering via Learning Cluster-level Representations—0
Improving Dense Contrastive Learning with Dense Negative Pairs—0
Improving Dialog Safety using Socially Aware Contrastive Learning—0
Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation—0
Bootstrapped Representation Learning on Graphs—0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning—0
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery—0
Game State Learning via Game Scene Augmentation—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Improving Generalizability of Protein Sequence Models via Data Augmentations—0
Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control—0
Contrastive Learning from Demonstrations—0
GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations—0
Improving Graph Contrastive Learning via Adaptive Positive Sampling—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Contrastive Learning for View Classification of Echocardiograms—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness—0
Contrastive Learning for Unsupervised Video Highlight Detection—0
A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
Improving Micro-video Recommendation by Controlling Position Bias—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Improving Neural Topic Models by Contrastive Learning with BERT—0
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition—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