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 2601–2650 of 6661 papers

TitleStatusHype
A Self-Learning Multimodal Approach for Fake News Detection—0
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation—0
Neighborhood Commonality-aware Evolution Network for Continuous Generalized Category DiscoveryCode0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
Unifying Dual-Space Embedding for Entity Alignment via Contrastive LearningCode0
DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval and Classification—0
SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB ImagesCode0
KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning—0
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning—0
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation—0
OCEAN: Open-World Contrastive Authorship IdentificationCode0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
Towards Zero-shot 3D Anomaly Localization—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
A Framework For Image Synthesis Using Supervised Contrastive Learning—0
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation—0
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static RelationsCode0
Enhancing CLIP Conceptual Embedding through Knowledge Distillation—0
Cluster Specific Representation Learning—0
Tight PAC-Bayesian Risk Certificates for Contrastive LearningCode0
TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception—0
Stain-aware Domain Alignment for Imbalance Blood Cell ClassificationCode0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
CLERF: Contrastive LEaRning for Full Range Head Pose Estimation—0
Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image SegmentationCode0
Memory-efficient Continual Learning with Neural Collapse Contrastive—0
Improving Language Transfer Capability of Decoder-only Architecture in Multilingual Neural Machine TranslationCode0
BANER: Boundary-Aware LLMs for Few-Shot Named Entity RecognitionCode0
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking—0
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries—0
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer—0
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection—0
Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution—0
Retrieval-guided Cross-view Image Synthesis—0
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet ExtractionCode0
FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation—0
Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
Effective Fine-Tuning of Vision-Language Models for Accurate Galaxy Morphology Analysis—0
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution—0
SAMa: Material-aware 3D Selection and Segmentation—0
FedRGL: Robust Federated Graph Learning for Label Noise—0
Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition—0
Novel Class Discovery for Open Set Raga Classification—0
Manual-PA: Learning 3D Part Assembly from Instruction Diagrams—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