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 6026–6050 of 6661 papers

TitleStatusHype
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings—0
Neighbour Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification—0
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition—0
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning—0
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning—0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
Large-Scale Hyperspectral Image Clustering Using Contrastive LearningCode0
Scaling Law for Recommendation Models: Towards General-purpose User Representations—0
Metric-based multimodal meta-learning for human movement identification via footstep recognition—0
Learning Representations for Pixel-based Control: What Matters and Why?—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes—0
A Multi-attribute Controllable Generative Model for Histopathology Image SynthesisCode0
SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval—0
Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels—0
Dual Prototypical Contrastive Learning for Few-shot Semantic SegmentationCode0
Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words—0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object ClassificationCode0
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
Effective Fine-Tuning Methods for Cross-lingual Adaptation—0
MiSS@WMT21: Contrastive Learning-reinforced Domain Adaptation in Neural Machine Translation—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