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 4376–4400 of 6661 papers

TitleStatusHype
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Control False Negative Instances In Contrastive Learning To ImproveLong-tailed Item Categorization—0
Control Image Captioning Spatially and Temporally—0
Controllable Augmentations for Video Representation Learning—0
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning—0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation—0
Convergence of End-to-End Training in Deep Unsupervised Contrastive Learning—0
Convergence of energy-based learning in linear resistive networks—0
Conversation Disentanglement with Bi-Level Contrastive Learning—0
ConVerSum: A Contrastive Learning-based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents—0
ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations—0
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture—0
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
Correlation-aware active learning for surgery video segmentation—0
CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging—0
CoSP: Co-supervised pretraining of pocket and ligand—0
COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport—0
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval—0
CoTSRF: Utilize Chain of Thought as Stealthy and Robust Fingerprint of Large Language Models—0
Show:102550
← PrevPage 176 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