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 3676–3700 of 6661 papers

TitleStatusHype
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples—0
Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives—0
NoteLLM: A Retrievable Large Language Model for Note Recommendation—0
Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity—0
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion—0
BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning—0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers—0
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral ImagesCode0
Partial Federated Learning—0
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional AdaptationCode0
SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos—0
LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation—0
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment AnalysisCode0
Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment—0
Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese—0
CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory Prediction—0
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media—0
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration—0
Speaker-Independent Dysarthria Severity Classification using Self-Supervised Transformers and Multi-Task Learning—0
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models—0
Debiased Novel Category Discovering and Localization—0
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic TransferCode0
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation—0
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—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