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 5776–5800 of 6661 papers

TitleStatusHype
Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores—0
Relative distance matters for one-shot landmark detection—0
Adaptive Discriminative Regularization for Visual Classification—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
Self-supervised Transformer for Deepfake Detection—0
Learning Moving-Object Tracking with FMCW LiDAR—0
The Optimal Noise in Noise-Contrastive Learning Is Not What You ThinkCode0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations—0
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering—0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
A Mutually Reinforced Framework for Pretrained Sentence Embeddings—0
Understanding Contrastive Learning Requires Incorporating Inductive Biases—0
Multi-Level Contrastive Learning for Cross-Lingual Alignment—0
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric—0
ARIA: Adversarially Robust Image Attribution for Content Provenance—0
Deep learning-based UAV detection in the low altitude clutter background—0
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning—0
Movies2Scenes: Using Movie Metadata to Learn Scene Representation—0
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation—0
CLSEG: Contrastive Learning of Story Ending GenerationCode0
Augment with Care: Contrastive Learning for Combinatorial ProblemsCode0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—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