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 5851–5900 of 6661 papers

TitleStatusHype
Multi-View Pre-Trained Model for Code Vulnerability Identification—0
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation—0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs—0
Music Era Recognition Using Supervised Contrastive Learning and Artist Information—0
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis—0
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading—0
Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification—0
MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation—0
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning—0
A Contrastive Learning Approach for Training Variational Autoencoder Priors—0
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos—0
Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries—0
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning—0
Negative Metric Learning for Graphs—0
Negative Prototypes Guided Contrastive Learning for WSOD—0
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition—0
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation—0
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation—0
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings—0
Neighbour Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification—0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis—0
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties—0
NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text—0
NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks—0
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning—0
Neuro-Symbolic Contrastive Learning for Cross-domain Inference—0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
NewsEmbed: Modeling News through Pre-trained Document Representations—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
Night-to-Day Translation via Illumination Degradation Disentanglement—0
NijiGAN: Transform What You See into Anime with Contrastive Semi-Supervised Learning and Neural Ordinary Differential Equations—0
NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification—0
Node Embeddings via Neighbor Embeddings—0
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models—0
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning—0
Noise-Robust Contrastive Learning—0
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision—0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices—0
No One Representation to Rule Them All: Overlapping Features of Training Methods—0
Not All Documents Are What You Need for Extracting Instruction Tuning Data—0
Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation—0
NoteLLM: A Retrievable Large Language Model for Note Recommendation—0
Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning—0
Novel Class Discovery for Open Set Raga Classification—0
Novelty Detection via Contrastive Learning with Negative Data Augmentation—0
Novelty Detection with Rotated Contrastive Predictive Coding—0
NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment—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