SOTAVerified

Representation Learning

Representation Learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like image classification and retrieval.

Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier.

Representation learning can be divided into:

  • Supervised representation learning: learning representations on task A using annotated data and used to solve task B
  • Unsupervised representation learning: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like GPT and BERT leverage unsupervised representation learning to tackle language tasks.

More recently, self-supervised learning (SSL) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP.

Here are some additional readings to go deeper on the task:

( Image credit: Visualizing and Understanding Convolutional Networks )

Papers

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Showing 6401–6450 of 10580 papers

TitleStatusHype
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text—0
Multi-modal Alignment using Representation Codebook—0
Multimodal and self-supervised representation learning for automatic gesture recognition in surgical robotics—0
Multi-modal Causal Structure Learning and Root Cause Analysis—0
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation—0
Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms—0
Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Image Annotation based on Deep Hierarchical Context Networks—0
Multimodal Contrastive Training for Visual Representation Learning—0
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics—0
Deep Q-Learning with Low Switching Cost—0
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation—0
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction—0
Multimodal deep representation learning for quantum cross-platform verification—0
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
Deep Prompt Tuning for Graph Transformers—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing—0
Multimodal Generative Models for Compositional Representation Learning—0
Boosting Team Modeling through Tempo-Relational Representation Learning—0
Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering—0
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation—0
IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets—0
Boosting ship detection in SAR images with complementary pretraining techniques—0
Multimodal Learning and Reasoning for Visual Question Answering—0
Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision—0
A Comprehensive Survey on Cross-modal Retrieval—0
Multimodal Machine Learning: Integrating Language, Vision and Speech—0
Composable Augmentation Encoding for Video Representation Learning—0
Multi-Modal Molecular Representation Learning via Structure Awareness—0
Neural Network Normal Estimation and Bathymetry Reconstruction from Sidescan Sonar—0
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition—0
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning—0
Neural News Recommendation with Topic-Aware News Representation—0
Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks—0
Multimodal Representation Learning and Fusion—0
Environment Predictive Coding for Visual Navigation—0
Episodes Discovery Recommendation with Multi-Source Augmentations—0
Multi-modal Representation Learning for Social Post Location Inference—0
IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations—0
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data—0
Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition—0
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging—0
Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision—0
Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8—Unverified
2SPECTERAvg.80—Unverified
3CiteomaticAvg.76—Unverified
4Sci-DeCLUTRAvg.66.6—Unverified
5SciBERTAvg.59.6—Unverified
6BioBERTAvg.58.8—Unverified
7CiteBERTAvg.58.8—Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05—Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56—Unverified