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 2326–2350 of 6661 papers

TitleStatusHype
ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution ErrorsCode0
Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark DatasetCode0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention HeadsCode0
Contrastive Learning-Based privacy metrics in Tabular Synthetic DatasetsCode0
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution GeneralizationCode0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views—0
UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code GenerationCode0
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond—0
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic ScreeningCode0
Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution SelectionCode0
Representation Learning on Out of Distribution in Tabular Data—0
SinSim: Sinkhorn-Regularized SimCLR—0
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation—0
Graph Diffusion Network for Drug-Gene PredictionCode0
GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation—0
Neuro-Symbolic Contrastive Learning for Cross-domain Inference—0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Generalized Class Discovery in Instance Segmentation—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