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 3926–3950 of 6661 papers

TitleStatusHype
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning—0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning—0
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems—0
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images—0
Breast tumor classification based on self-supervised contrastive learning from ultrasound videos—0
Bridge the Gap between Language models and Tabular Understanding—0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application—0
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries—0
Bridging Text and Image for Artist Style Transfer via Contrastive Learning—0
Bridging the Emotional Semantic Gap via Multimodal Relevance Estimation—0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning—0
Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching—0
BRIDO: Bringing Democratic Order to Abstractive Summarization—0
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation—0
Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis—0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering—0
Building an Enhanced Autoregressive Document Retriever Leveraging Supervised Contrastive Learning—0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks—0
Building Vision-Language Models on Solid Foundations with Masked Distillation—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