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 826–850 of 6661 papers

TitleStatusHype
Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation—0
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?Code0
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy—0
Dynamic Modality-Camera Invariant Clustering for Unsupervised Visible-Infrared Person Re-identification—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
AmCLR: Unified Augmented Learning for Cross-Modal RepresentationsCode0
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning—0
Learning Self-Supervised Audio-Visual Representations for Sound Recommendations—0
Image Retrieval with Intra-Sweep Representation Learning for Neck Ultrasound Scanning Guidance—0
Multi-Scale Contrastive Learning for Video Temporal Grounding—0
Fine-grained Text to Image Synthesis—0
StyleMaster: Stylize Your Video with Artistic Generation and Translation—0
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence—0
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training—0
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation—0
Measuring Pre-training Data Quality without Labels for Time Series Foundation Models—0
A Self-Learning Multimodal Approach for Fake News Detection—0
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
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
Neighborhood Commonality-aware Evolution Network for Continuous Generalized Category DiscoveryCode0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
Unifying Dual-Space Embedding for Entity Alignment via Contrastive LearningCode0
SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB ImagesCode0
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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