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 3251–3300 of 6661 papers

TitleStatusHype
Learning to Anticipate Egocentric Actions by Imagination—0
Learning Tracking Representations from Single Point Annotations—0
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—0
Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments—0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data—0
From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Learning Sound Localization Better From Semantically Similar Samples—0
Contrastive Learning for Regression on Hyperspectral Data—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
From Fake to Hyperpartisan News Detection Using Domain Adaptation—0
Intent-Enhanced Data Augmentation for Sequential Recommendation—0
Learning Spatially-Aware Language and Audio Embeddings—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps—0
Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging—0
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
Contrastive Learning for Recommender System—0
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration—0
Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning—0
Boosting Semi-Supervised Learning with Contrastive Complementary Labeling—0
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning—0
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning—0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification—0
Learning Reward Functions for Robotic Manipulation by Observing Humans—0
ACC-ViT : Atrous Convolution's Comeback in Vision Transformers—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Learning Self-Supervised Audio-Visual Representations for Sound Recommendations—0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation—0
CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis—0
C-SENN: Contrastive Self-Explaining Neural Network—0
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models—0
Frequency-Aware Contrastive Learning for Neural Machine Translation—0
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning—0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning—0
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks—0
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition—0
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views—0
Learning Representations for Pixel-based Control: What Matters and Why?—0
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022—0
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis—0
Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition—0
Learning Referring Video Object Segmentation from Weak Annotation—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