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 1751–1775 of 6661 papers

TitleStatusHype
Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation PretrainingCode1
Towards Ontology-Enhanced Representation Learning for Large Language ModelsCode0
PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting—0
Relation Modeling and Distillation for Learning with Noisy Labels—0
May the Dance be with You: Dance Generation Framework for Non-Humanoids—0
Video-Language Critic: Transferable Reward Functions for Language-Conditioned RoboticsCode0
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive LearningCode1
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity—0
Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology DetectionCode0
Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing—0
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation—0
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification—0
Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning—0
Relational Self-supervised Distillation with Compact Descriptors for Image Copy DetectionCode0
SSLChange: A Self-supervised Change Detection Framework Based on Domain AdaptationCode1
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory SignalsCode2
OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects SupervisionCode1
On the Sequence Evaluation based on Stochastic Processes—0
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word ExclusionCode1
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based LossesCode1
LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent SpaceCode1
A Vlogger-augmented Graph Neural Network Model for Micro-video RecommendationCode0
Finding Shared Decodable Concepts and their Negations in the Brain—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