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 5751–5800 of 6661 papers

TitleStatusHype
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation—0
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial TrainingCode0
Supervised Contrastive Learning with Structure Inference for Graph Classification—0
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning—0
Better Quality Estimation for Low Resource Corpus Mining—0
Contrastive Learning of Sociopragmatic Meaning in Social MediaCode0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Multi-View Dreaming: Multi-View World Model with Contrastive Learning—0
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition—0
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification—0
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation—0
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation—0
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation—0
3SD: Self-Supervised Saliency Detection With No LabelsCode0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Predicting conversion of mild cognitive impairment to Alzheimer's disease—0
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading—0
Contrastive Conditional Neural Processes—0
Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding—0
Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategiesCode0
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation—0
Learning to Ground Decentralized Multi-Agent Communication with Contrastive Learning—0
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning—0
MixCL: Pixel label matters to contrastive learning—0
Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores—0
Relative distance matters for one-shot landmark detection—0
Adaptive Discriminative Regularization for Visual Classification—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
Self-supervised Transformer for Deepfake Detection—0
Learning Moving-Object Tracking with FMCW LiDAR—0
The Optimal Noise in Noise-Contrastive Learning Is Not What You ThinkCode0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations—0
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering—0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
A Mutually Reinforced Framework for Pretrained Sentence Embeddings—0
Understanding Contrastive Learning Requires Incorporating Inductive Biases—0
Multi-Level Contrastive Learning for Cross-Lingual Alignment—0
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric—0
ARIA: Adversarially Robust Image Attribution for Content Provenance—0
Deep learning-based UAV detection in the low altitude clutter background—0
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning—0
Movies2Scenes: Using Movie Metadata to Learn Scene Representation—0
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation—0
CLSEG: Contrastive Learning of Story Ending GenerationCode0
Augment with Care: Contrastive Learning for Combinatorial ProblemsCode0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—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