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 2001–2025 of 6661 papers

TitleStatusHype
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification—0
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
On the Surprising Efficacy of Distillation as an Alternative to Pre-Training Small ModelsCode0
Generative-Contrastive Heterogeneous Graph Neural NetworkCode0
GenN2N: Generative NeRF2NeRF TranslationCode2
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human RationalesCode0
Large Language Models for Expansion of Spoken Language Understanding Systems to New LanguagesCode1
A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware CapabilityCode0
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement—0
ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design ModelsCode1
A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image ClassificationCode0
Iterated Learning Improves Compositionality in Large Vision-Language Models—0
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive LearningCode0
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels—0
SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining—0
S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images—0
Language Guided Domain Generalized Medical Image SegmentationCode1
Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive LearningCode0
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Heterogeneous Contrastive Learning for Foundation Models and BeyondCode1
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-trainingCode0
Classification and Clustering of Sentence-Level Embeddings of Scientific Articles Generated by Contrastive Learning—0
Robust Federated Contrastive Recommender System against Model Poisoning Attack—0
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in ConversationCode1
Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover ClassificationCode0
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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