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 2126–2150 of 6661 papers

TitleStatusHype
Dialogue Response Generation via Contrastive Latent Representation Learning—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Focus-Driven Contrastive Learning for Medical Question Summarization—0
Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows—0
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse—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
Action-based Contrastive Learning for Trajectory Prediction—0
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning—0
FMiFood: Multi-modal Contrastive Learning for Food Image Classification—0
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning—0
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning—0
FMP: Toward Fair Graph Message Passing against Topology Bias—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