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 5751–5775 of 6661 papers

TitleStatusHype
Contrastive Learning is Just Meta-Learning—0
Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
Contrastive Label Disambiguation for Partial Label LearningCode1
Multi-Domain Self-Supervised Learning—0
Information-Aware Time Series Meta-Contrastive Learning—0
Self-Contrastive Learning—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
A Rate-Distortion Approach to Domain Generalization—0
Context-invariant, multi-variate time series representations—0
SimMER: Simple Maximization of Entropy and Rank for Self-supervised Representation Learning—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Chaos is a Ladder: A New Understanding of Contrastive Learning—0
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning—0
Approximate Bijective Correspondence for isolating factors of variationCode0
Understanding Self-supervised Learning via Information Bottleneck Principle—0
Rethinking Temperature in Graph Contrastive LearningCode0
Inductive-Biases for Contrastive Learning of Disentangled Representations—0
f-Mutual Information Contrastive Learning—0
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations—0
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation—0
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—0
Contrastive Pre-training for Zero-Shot Information Retrieval—0
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning—0
How does Contrastive Pre-training Connect Disparate Domains?—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