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 8201–8250 of 10580 papers

TitleStatusHype
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models—0
Canonical Latent Representations in Conditional Diffusion Models—0
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications—0
JOOCI: a Framework for Learning Comprehensive Speech Representations—0
Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning—0
Joint Syntax Representation Learning and Visual Cue Translation for Video Captioning—0
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling—0
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering—0
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning—0
Dilated Strip Attention Network for Image Restoration—0
Canonical Correlation Guided Deep Neural Network—0
Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion—0
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision—0
Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data—0
Joint Registration and Representation Learning for Unconstrained Face Identification—0
Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content—0
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?—0
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing—0
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces—0
Diffusion Spectral Representation for Reinforcement Learning—0
Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training—0
Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal Sentence Localization in Videos—0
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning—0
Jointly spatial-temporal representation learning for individual trajectories—0
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning—0
Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification—0
Joint Learning of Local and Global Features for Aspect-based Sentiment Classification—0
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach—0
Mixture Representation Learning with Coupled Autoencoders—0
Joint Learning in the Gaussian Single Index Model—0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?—0
Can Contrastive Learning Refine Embeddings—0
Joint Learning from Labeled and Unlabeled Data for Information Retrieval—0
Joint image reconstruction and segmentation of real-time cardiac MRI in free-breathing using a model based on disentangled representation learning—0
Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology—0
DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion—0
Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection—0
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies—0
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning—0
基於深層類神經網路及表示學習技術之文件可讀性分類(Classification of Text Readability Based on Deep Neural Network and Representation Learning Techniques)[In Chinese]—0
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction—0
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain—0
Joint embedding in Hierarchical distance and semantic representation learning for link prediction—0
Joint Debiased Representation and Image Clustering Learning with Self-Supervision—0
Diffusion Based Causal Representation Learning—0
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds—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
6CiteBERTAvg.58.8—Unverified
7BioBERTAvg.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