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 5001–5050 of 6661 papers

TitleStatusHype
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Towards the Sparseness of Projection Head in Self-Supervised Learning—0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method—0
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis—0
Towards Universal GAN Image Detection—0
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting—0
Towards Zero-shot 3D Anomaly Localization—0
Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path—0
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning—0
TPDR: A Novel Two-Step Transformer-based Product and Class Description Match and Retrieval Method—0
TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation—0
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions—0
TRACE: Contrastive learning for multi-trial time-series data in neuroscience—0
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs—0
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses—0
TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction—0
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study—0
TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes—0
Traffic Scene Similarity: a Graph-based Contrastive Learning Approach—0
Training Dynamics of Nonlinear Contrastive Learning Model in the High Dimensional Limit—0
TransAug: Translate as Augmentation for Sentence Embeddings—0
Transductive CLIP with Class-Conditional Contrastive Learning—0
Transferability of Representations Learned using Supervised Contrastive Learning Trained on a Multi-Domain Dataset—0
Transfer-Free Data-Efficient Multilingual Slot Labeling—0
Transferrable Contrastive Learning for Visual Domain Adaptation—0
Transformation of audio embeddings into interpretable, concept-based representations—0
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval—0
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training—0
Transformer-Empowered Content-Aware Collaborative Filtering—0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers—0
Deep Fusion: Capturing Dependencies in Contrastive Learning via Transformer Projection Heads—0
Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding—0
t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving—0
TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception—0
Triangular Contrastive Learning on Molecular Graphs—0
TriCoLo: Trimodal Contrastive Loss for Text to Shape Retrieval—0
TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction—0
Triple Sequence Learning for Cross-domain Recommendation—0
Triplet Contrastive Learning for Brain Tumor Classification—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
TRRG: Towards Truthful Radiology Report Generation With Cross-modal Disease Clue Enhanced Large Language Model—0
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos—0
Trunk-branch Contrastive Network with Multi-view Deformable Aggregation for Multi-view Action Recognition—0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
TULIP: Towards Unified Language-Image Pretraining—0
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—0
Tuned Contrastive Learning—0
Turbo your multi-modal classification with contrastive learning—0
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data—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