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 3401–3425 of 6661 papers

TitleStatusHype
Rehearsal-free Federated Domain-incremental Learning—0
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation—0
A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data—0
Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous DataCode0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation—0
C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning—0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis—0
Efficient Model-Stealing Attacks Against Inductive Graph Neural NetworksCode0
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification—0
Unveiling Key Aspects of Fine-Tuning in Sentence Embeddings: A Representation Rank Analysis—0
LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning—0
INDUS: Effective and Efficient Language Models for Scientific Applications—0
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
Automated Radiology Report Generation: A Review of Recent Advances—0
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection—0
AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning—0
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning—0
Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future RepresentationsCode0
Learning Generalized Medical Image Representations through Image-Graph Contrastive Pretraining—0
Self-Distillation Improves DNA Sequence InferenceCode0
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content—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