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 3201–3225 of 6661 papers

TitleStatusHype
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—0
Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action LocalizationCode0
Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework—0
Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification—0
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement—0
CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging—0
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?—0
EA-VTR: Event-Aware Video-Text Retrieval—0
Resolving Sentiment Discrepancy for Multimodal Sentiment Detection via Semantics Completion and Decomposition—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
Sequential Contrastive Audio-Visual Learning—0
Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024Code0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
Contrastive Learning of Preferences with a Contextual InfoNCE Loss—0
An accurate detection is not all you need to combat label noise in web-noisy datasetsCode0
Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRICode0
Self-Paced Sample Selection for Barely-Supervised Medical Image SegmentationCode0
Music Era Recognition Using Supervised Contrastive Learning and Artist Information—0
Online Drift Detection with Maximum Concept DiscrepancyCode0
The Solution for Language-Enhanced Image New Category Discovery—0
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs—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