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 2601–2625 of 6661 papers

TitleStatusHype
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation—0
MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-trainingCode0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
Neighborhood Commonality-aware Evolution Network for Continuous Generalized Category DiscoveryCode0
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
OCEAN: Open-World Contrastive Authorship IdentificationCode0
DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval and Classification—0
Unifying Dual-Space Embedding for Entity Alignment via Contrastive LearningCode0
KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning—0
SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB ImagesCode0
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning—0
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
Towards Zero-shot 3D Anomaly Localization—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
A Framework For Image Synthesis Using Supervised Contrastive Learning—0
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation—0
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static RelationsCode0
Enhancing CLIP Conceptual Embedding through Knowledge Distillation—0
Cluster Specific Representation Learning—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
Tight PAC-Bayesian Risk Certificates for Contrastive LearningCode0
TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception—0
Stain-aware Domain Alignment for Imbalance Blood Cell ClassificationCode0
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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