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 4751–4800 of 10580 papers

TitleStatusHype
Direction-Aware Hybrid Representation Learning for 3D Hand Pose and Shape Estimation—0
Directional Sign Loss: A Topology-Preserving Loss Function that Approximates the Sign of Finite Differences—0
Can Temporal Information Help with Contrastive Self-Supervised Learning?—0
Directional Self-supervised Learning for Heavy Image Augmentations—0
Directionally Convolutional Networks for 3D Shape Segmentation—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
Directional diffusion models for graph representation learning—0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Adversarial representation learning for synthetic replacement of private attributes—0
Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding—0
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
Apparel-invariant Feature Learning for Apparel-changed Person Re-identification—0
A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder—0
Large-Scale Unsupervised Deep Representation Learning for Brain Structure—0
An Improved Semi-Supervised VAE for Learning Disentangled Representations—0
Latent Representation Learning for Multimodal Brain Activity Translation—0
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?—0
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach—0
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning—0
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications—0
Improving Discrete Latent Representations With Differentiable Approximation Bridges—0
DINE: A Framework for Deep Incomplete Network Embedding—0
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models—0
Canonical Latent Representations in Conditional Diffusion Models—0
LARGE SCALE REPRESENTATION LEARNING FROM TRIPLET COMPARISONS—0
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation—0
Canonical Correlation Guided Deep Neural Network—0
Dilated Strip Attention Network for Image Restoration—0
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?—0
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces—0
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing—0
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers—0
Large-scale representation learning from visually grounded untranscribed speech—0
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report—0
Diffusion Spectral Representation for Reinforcement Learning—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning—0
Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement—0
A Co-training Approach for Noisy Time Series Learning—0
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders—0
Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification—0
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?—0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Can Contrastive Learning Refine Embeddings—0
Large-Scale Few-Shot Classification with Semi-supervised Hierarchical k-Probabilistic PCAs—0
DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion—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
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning—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