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 4151–4175 of 6661 papers

TitleStatusHype
MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant FeaturesCode0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation—0
Multi-view Contrastive Learning for Entity Typing over Knowledge GraphsCode0
Open-Set Multivariate Time-Series Anomaly Detection—0
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction FollowingCode0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation LearningCode0
Node-based Knowledge Graph Contrastive Learning for Medical Relationship PredictionCode0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
BeatDance: A Beat-Based Model-Agnostic Contrastive Learning Framework for Music-Dance Retrieval—0
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural NetworksCode0
DropMix: Better Graph Contrastive Learning with Harder Negative SamplesCode0
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings—0
Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering—0
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation—0
Large Language Model-Aware In-Context Learning for Code Generation—0
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised RankingCode0
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos—0
Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context—0
Semi-Supervised End-To-End Contrastive Learning For Time Series Classification—0
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport—0
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images—0
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation—0
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations—0
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval—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