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 3976–4000 of 6661 papers

TitleStatusHype
Causality-based Dual-Contrastive Learning Framework for Domain Generalization—0
Causality-inspired Discriminative Feature Learning in Triple Domains for Gait Recognition—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
CCFC++: Enhancing Federated Clustering through Feature Decorrelation—0
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation—0
CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory Prediction—0
CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation—0
CDA: Contrastive-adversarial Domain Adaptation—0
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery—0
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning—0
Center Contrastive Loss for Metric Learning—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
CFTrack: Enhancing Lightweight Visual Tracking through Contrastive Learning and Feature Matching—0
CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Challenging Low Homophily in Social Recommendation—0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
Chaos is a Ladder: A New Understanding of Contrastive Learning—0
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization—0
3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning—0
ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language—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