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 3551–3575 of 6661 papers

TitleStatusHype
Enhancing Adversarial Contrastive Learning via Adversarial Invariant RegularizationCode1
LostPaw: Finding Lost Pets using a Contrastive Learning-based Transformer with Visual InputCode0
SGAligner : 3D Scene Alignment with Scene GraphsCode1
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information—0
ContraNeRF: 3D-Aware Generative Model via Contrastive Learning with Unsupervised Implicit Pose Embedding—0
MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive LearningCode0
Self-Supervised Multi-Modal Sequential RecommendationCode1
All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement—0
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography—0
Sample-Specific Debiasing for Better Image-Text Models—0
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds—0
Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints—0
OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification—0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular SynthesisCode1
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation—0
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network—0
Constructing Tree-based Index for Efficient and Effective Dense RetrievalCode1
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation—0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition—0
Unsupervised CD in satellite image time series by contrastive learning and feature tracking—0
Spectral Normalization and Dual Contrastive Regularization for Image-to-Image TranslationCode0
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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