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 1176–1200 of 6661 papers

TitleStatusHype
Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic InformationCode0
Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization—0
Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats—0
Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery—0
TwinCL: A Twin Graph Contrastive Learning Model for Collaborative FilteringCode0
Embed and Emulate: Contrastive representations for simulation-based inference—0
Reducing Semantic Ambiguity In Domain Adaptive Semantic Segmentation Via Probabilistic Prototypical Pixel ContrastCode0
UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion PerceptionCode0
You Only Speak Once to See—0
Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks—0
Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal ClassificationCode0
Robotic-CLIP: Fine-tuning CLIP on Action Data for Robotic Applications—0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning—0
Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine SegmentationCode0
Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data—0
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI GenerationCode1
DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare DataCode1
Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image EnhancementCode1
DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation—0
Enhanced Unsupervised Image-to-Image Translation Using Contrastive Learning and Histogram of Oriented Gradients—0
Patch-Based Contrastive Learning and Memory Consolidation for Online Unsupervised Continual LearningCode0
PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings—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