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 6201–6225 of 6661 papers

TitleStatusHype
Rethinking Weak Supervision in Helping Contrastive Learning—0
Rethinking Weak-to-Strong Augmentation in Source-Free Domain Adaptive Object Detection—0
Retrieval Augmented Generation for Dynamic Graph Modeling—0
Retrieval-guided Cross-view Image Synthesis—0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
Retrieving Time-Series Differences Using Natural Language Queries—0
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning—0
Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning—0
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition—0
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning—0
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework—0
Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering—0
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition—0
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements—0
Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification—0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum—0
Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)—0
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding using Contrastive Learning with Application to Room Shape Classification—0
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing—0
RingMo-Aerial: An Aerial Remote Sensing Foundation Model With A Affine Transformation Contrastive Learning—0
RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning—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