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 3351–3375 of 6661 papers

TitleStatusHype
Causal Contrastive Learning for Counterfactual Regression Over Time—0
Contrastive Learning Via Equivariant Representation—0
Self-degraded contrastive domain adaptation for industrial fault diagnosis with bi-imbalanced data—0
Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning—0
Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation LearningCode0
Heterophilous Distribution Propagation for Graph Neural Networks—0
Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness EstimationCode0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning—0
Popularity-Aware Alignment and Contrast for Mitigating Popularity BiasCode0
May the Dance be with You: Dance Generation Framework for Non-Humanoids—0
Medication Recommendation via Dual Molecular Modalities and Multi-Step EnhancementCode0
PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting—0
Relation Modeling and Distillation for Learning with Noisy Labels—0
Video-Language Critic: Transferable Reward Functions for Language-Conditioned RoboticsCode0
Towards Ontology-Enhanced Representation Learning for Large Language ModelsCode0
Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing—0
Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology DetectionCode0
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity—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
On the Sequence Evaluation based on Stochastic Processes—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
Relational Self-supervised Distillation with Compact Descriptors for Image Copy DetectionCode0
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning—0
A Vlogger-augmented Graph Neural Network Model for Micro-video RecommendationCode0
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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