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 1551–1575 of 6661 papers

TitleStatusHype
Multimodal contrastive learning for spatial gene expression prediction using histology imagesCode1
TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete DataCode2
CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging—0
EA-VTR: Event-Aware Video-Text Retrieval—0
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?—0
ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic SegmentationCode1
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
HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space ModelCode1
Resolving Sentiment Discrepancy for Multimodal Sentiment Detection via Semantics Completion and Decomposition—0
Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024Code0
Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRICode0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
An accurate detection is not all you need to combat label noise in web-noisy datasetsCode0
Sequential Contrastive Audio-Visual Learning—0
Training-free CryoET Tomogram SegmentationCode2
4D Contrastive Superflows are Dense 3D Representation LearnersCode2
Contrastive Learning of Preferences with a Contextual InfoNCE Loss—0
Self-Paced Sample Selection for Barely-Supervised Medical Image SegmentationCode0
Online Drift Detection with Maximum Concept DiscrepancyCode0
CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTsCode1
Language Representations Can be What Recommenders Need: Findings and PotentialsCode2
Music Era Recognition Using Supervised Contrastive Learning and Artist Information—0
A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning—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