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 3426–3450 of 6661 papers

TitleStatusHype
Self-supervised contrastive learning unveils cortical folding pattern linked to prematurityCode0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
T3RD: Test-Time Training for Rumor Detection on Social MediaCode0
Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Machine Unlearning in Contrastive Learning—0
PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix AugmentationCode0
HC^2L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding—0
Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model—0
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—0
Towards Less Biased Data-driven Scoring with Deep Learning-Based End-to-end Database Search in Tandem Mass Spectrometry—0
Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information—0
Graded Relevance Scoring of Written Essays with Dense Retrieval—0
Dual-domain Collaborative Denoising for Social Recommendation—0
Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation—0
Multi-Margin Cosine Loss: Proposal and Application in Recommender SystemsCode0
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures—0
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images—0
Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-TuningCode0
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document UnderstandingCode0
Improved Forward-Forward Contrastive Learning—0
A Generalization Theory of Cross-Modality Distillation with Contrastive Learning—0
Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity—0
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—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