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 3476–3500 of 6661 papers

TitleStatusHype
SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings—0
Learning Discriminative Spatio-temporal Representations for Semi-supervised Action Recognition—0
FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art CommissionsCode0
Mixed Supervised Graph Contrastive Learning for Recommendation—0
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data—0
Towards Universal Dense Blocking for Entity ResolutionCode0
CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios—0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
SI-FID: Only One Objective Indicator for Evaluating Stitched Images—0
CKD: Contrastive Knowledge Distillation from A Sample-wise PerspectiveCode0
Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning—0
Video sentence grounding with temporally global textual knowledge—0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
Collaborative Visual Place Recognition through Federated Learning—0
CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts—0
Improving Pediatric Pneumonia Diagnosis with Adult Chest X-ray Images Utilizing Contrastive Learning and Embedding Similarity—0
Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table RepresentationsCode0
Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation—0
Leveraging Intra-modal and Inter-modal Interaction for Multi-Modal Entity Alignment—0
Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion ModelsCode0
TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction—0
Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation—0
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge—0
Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate RainsCode0
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification—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