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 5201–5225 of 6661 papers

TitleStatusHype
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation—0
iEdit: Localised Text-guided Image Editing with Weak Supervision—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Image Classification Using a Diffusion Model as a Pre-Training Model—0
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation—0
Image Harmonization with Region-wise Contrastive Learning—0
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment—0
Image Reconstruction as a Tool for Feature Analysis—0
Image Retrieval with Intra-Sweep Representation Learning for Neck Ultrasound Scanning Guidance—0
StackMix: A complementary Mix algorithm—0
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications—0
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification—0
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models—0
Imitation from Observation With Bootstrapped Contrastive Learning—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Impact-driven Exploration with Contrastive Unsupervised Representations—0
Impact of Language Guidance: A Reproducibility Study—0
Implicit Surface Contrastive Clustering for LiDAR Point Clouds—0
Improved baselines for vision-language pre-training—0
LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder—0
Improved Forward-Forward Contrastive Learning—0
Improved Text Classification via Contrastive Adversarial Training—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