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 6251–6275 of 6661 papers

TitleStatusHype
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations—0
Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning—0
RSAM: Learning on manifolds with Riemannian Sharpness-aware Minimization—0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach—0
RUSH: Robust Contrastive Learning via Randomized Smoothing—0
S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images—0
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning—0
S^3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification—0
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction—0
SALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data—0
Saliency Guided Contrastive Learning on Scene Images—0
SaliencyI2PLoc: saliency-guided image-point cloud localization using contrastive learning—0
SAMa: Material-aware 3D Selection and Segmentation—0
SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling—0
SampleMatch: Drum Sample Retrieval by Musical Context—0
Sample-Specific Debiasing for Better Image-Text Models—0
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives—0
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning—0
Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images—0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping—0
SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing—0
Scalable Deep Metric Learning on Attributed Graphs—0
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—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