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

Showing 10011025 of 6661 papers

TitleStatusHype
Aligning Pretraining for Detection via Object-Level Contrastive LearningCode1
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited AnnotationsCode1
Contrastive Learning for Cross-Domain Open World RecognitionCode1
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive LearningCode1
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based FeaturesCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
A Message Passing Perspective on Learning Dynamics of Contrastive LearningCode1
Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal ContrastCode1
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive LearningCode1
Learning Visual Representations via Language-Guided SamplingCode1
AASAE: Augmentation-Augmented Stochastic AutoencodersCode1
Learning with Partial Labels from Semi-supervised PerspectiveCode1
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus ImagesCode1
Let Invariant Rationale Discovery Inspire Graph Contrastive LearningCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
Contrastive Learning for Improving ASR Robustness in Spoken Language UnderstandingCode1
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal GroundingCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D DatasetsCode1
CoMatch: Semi-supervised Learning with Contrastive Graph RegularizationCode1
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism SynthesisCode1
LipLearner: Customizable Silent Speech Interactions on Mobile DevicesCode1
Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic SegmentationCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified