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 5876–5900 of 6661 papers

TitleStatusHype
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning—0
Neuro-Symbolic Contrastive Learning for Cross-domain Inference—0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
NewsEmbed: Modeling News through Pre-trained Document Representations—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
Night-to-Day Translation via Illumination Degradation Disentanglement—0
NijiGAN: Transform What You See into Anime with Contrastive Semi-Supervised Learning and Neural Ordinary Differential Equations—0
NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification—0
Node Embeddings via Neighbor Embeddings—0
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models—0
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning—0
Noise-Robust Contrastive Learning—0
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision—0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices—0
No One Representation to Rule Them All: Overlapping Features of Training Methods—0
Not All Documents Are What You Need for Extracting Instruction Tuning Data—0
Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation—0
NoteLLM: A Retrievable Large Language Model for Note Recommendation—0
Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning—0
Novel Class Discovery for Open Set Raga Classification—0
Novelty Detection via Contrastive Learning with Negative Data Augmentation—0
Novelty Detection with Rotated Contrastive Predictive Coding—0
NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment—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