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 4176–4200 of 6661 papers

TitleStatusHype
Continual Contrastive Spoken Language Understanding—0
Continual Learning for Temporal-Sensitive Question Answering—0
Continual Multimodal Contrastive Learning—0
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
ContraNeRF: 3D-Aware Generative Model via Contrastive Learning with Unsupervised Implicit Pose Embedding—0
ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning—0
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration—0
ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion—0
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection—0
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing—0
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories—0
ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning—0
Contrast Is All You Need—0
Contrastive Abstraction for Reinforcement Learning—0
Contrastive Adapters for Foundation Model Group Robustness—0
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification—0
Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology—0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Contrastive Classification and Representation Learning with Probabilistic Interpretation—0
Contrastive Coding for Active Learning Under Class Distribution Mismatch—0
Contrastive Conditional Masked Language Model for Non-autoregressive Neural Machine Translation—0
Contrastive Conditional Neural Processes—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