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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3101–3125 of 6661 papers

TitleStatusHype
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—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
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning—0
Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation—0
Bootstrapped Representation Learning on Graphs—0
Improving Continual Relation Extraction through Prototypical Contrastive Learning—0
Language-guided Image Reflection Separation—0
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning—0
Large Language Model-Aware In-Context Learning for Code Generation—0
Game State Learning via Game Scene Augmentation—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 Contrastive Learning on Visually Homogeneous Mars Rover Images—0
Contrastive Learning for View Classification of Echocardiograms—0
Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation—0
Improving Cross-Modal Understanding in Visual Dialog via Contrastive 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
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
Show:102550
← PrevPage 125 of 267Next →
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