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 3301–3325 of 6661 papers

TitleStatusHype
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking—0
Dialogue Response Generation via Contrastive Latent Representation Learning—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation—0
DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation—0
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations—0
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling—0
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings—0
A Topic-aware Summarization Framework with Different Modal Side Information—0
DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection—0
DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art—0
CLoCE:Contrastive Learning Optimize Continous Prompt Embedding Space in Relation Extraction—0
ATM-Net: Anatomy-Aware Text-Guided Multi-Modal Fusion for Fine-Grained Lumbar Spine Segmentation—0
AI Foundation Models in Remote Sensing: A Survey—0
Developing Healthcare Language Model Embedding Spaces—0
CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss—0
Detect Low-Resource Rumors in Microblog Posts via Adversarial Contrastive Learning—0
ATM: Action Temporality Modeling for Video Question Answering—0
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning—0
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning—0
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning—0
Action-based Contrastive Learning for Trajectory Prediction—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—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