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 2001–2050 of 6661 papers

TitleStatusHype
BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive LearningCode0
Improving Contrastive Learning for Referring Expression CountingCode0
Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive LearningCode0
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration—0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Supervised Contrastive Learning for Ordinal Engagement Measurement—0
Aligning Proteins and Language: A Foundation Model for Protein Retrieval—0
Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval—0
Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory QueueCode0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal ModelsCode0
Can Visual Encoder Learn to See Arrows?—0
AmpleHate: Amplifying the Attention for Versatile Implicit Hate DetectionCode0
Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation LearningCode0
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense RetrievalCode0
HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation—0
Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image SegmentationCode0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
Learn Beneficial Noise as Graph Augmentation—0
Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic DataCode0
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging—0
Paying Alignment Tax with Contrastive Learning—0
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge CollaborationCode0
AmorLIP: Efficient Language-Image Pretraining via AmortizationCode0
Manifold-aware Representation Learning for Degradation-agnostic Image Restoration—0
Grounding Bodily Awareness in Visual Representations for Efficient Policy LearningCode0
WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and SegmentationCode0
Supervised Graph Contrastive Learning for Gene Regulatory Network—0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
Learning Genomic Structure from k-mers—0
CoTSRF: Utilize Chain of Thought as Stealthy and Robust Fingerprint of Large Language Models—0
Visual Perturbation and Adaptive Hard Negative Contrastive Learning for Compositional Reasoning in Vision-Language ModelsCode0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives—0
Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents—0
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives—0
FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive LearningCode0
Towards Generating Realistic Underwater Images—0
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning—0
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search—0
Unsupervised Graph Clustering with Deep Structural EntropyCode0
Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive LearningCode0
Representation of perceived prosodic similarity of conversational feedback—0
Self-Reinforced Graph Contrastive LearningCode0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping—0
The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive LearningCode0
Multiscale Adaptive Conflict-Balancing Model For Multimedia Deepfake Detection—0
GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval—0
Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning—0
SPKLIP: Aligning Spike Video Streams with Natural Language—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