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 2176–2200 of 6661 papers

TitleStatusHype
Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-DiversityCode0
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection—0
All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning—0
SPF-Portrait: Towards Pure Portrait Customization with Semantic Pollution-Free Fine-tuning—0
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
Node Embeddings via Neighbor Embeddings—0
Consistent Subject Generation via Contrastive Instantiated Concepts—0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering—0
CrossMuSim: A Cross-Modal Framework for Music Similarity Retrieval with LLM-Powered Text Description Sourcing and Mining—0
Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of RelevanceCode0
Fuzzy Cluster-Aware Contrastive Clustering for Time SeriesCode0
Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankersCode0
Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach—0
FakeReasoning: Towards Generalizable Forgery Detection and Reasoning—0
Retrieving Time-Series Differences Using Natural Language Queries—0
NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text—0
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
DINeMo: Learning Neural Mesh Models with no 3D Annotations—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Deep Learning for Forensic Identification of SourceCode0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
Taxonomy Inference for Tabular Data Using Large Language Models—0
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI—0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
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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