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 3476–3500 of 6661 papers

TitleStatusHype
Visual Imitation Learning with Calibrated Contrastive Representation—0
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks—0
Visual Question Answering in the Medical Domain—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
Visual Transformation Aided Contrastive Learning for Video-Based Kinship Verification—0
VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval—0
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix—0
VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos—0
Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation—0
Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation—0
VPN: Video Provenance Network for Robust Content Attribution—0
Wasserstein Contrastive Representation Distillation—0
Watching Too Much Television is Good: Self-Supervised Audio-Visual Representation Learning from Movies and TV Shows—0
Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation—0
Watermarking Pre-trained Encoders in Contrastive Learning—0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition—0
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition—0
Weakly-Supervised Audio-Visual Segmentation—0
Weakly-Supervised Audio-Visual Video Parsing with Prototype-based Pseudo-Labeling—0
Weakly-supervised Generative Adversarial Networks for medical image classification—0
Weakly Supervised Person Search with Region Siamese Networks—0
Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information—0
Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining—0
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding—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