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 6501–6550 of 10580 papers

TitleStatusHype
Multiscale Audio Spectrogram Transformer for Efficient Audio Classification—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
Multiscale Multimodal Transformer for Multimodal Action Recognition—0
Multi-Scale Neural network for EEG Representation Learning in BCI—0
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework—0
Learning to Predict Activity Progress by Self-Supervised Video Alignment—0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making—0
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning—0
CORAL: Concept Drift Representation Learning for Co-evolving Time-series—0
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy—0
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity—0
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity—0
DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences—0
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition—0
Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning—0
Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification—0
Multi-Step Prediction in Linearized Latent State Spaces for Representation Learning—0
Choose What You Need: Disentangled Representation Learning for Scene Text Recognition Removal and Editing—0
Learning to Learn with Conditional Class Dependencies—0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—0
DREAM: A Dual Representation Learning Model for Multimodal Recommendation—0
ROOTS: Object-Centric Representation and Rendering of 3D Scenes—0
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations—0
DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal—0
Choose What You Need: Disentangled Representation Learning for Scene Text Recognition, Removal and Editing—0
A Self-supervised Mixed-curvature Graph Neural Network—0
A Foundational Brain Dynamics Model via Stochastic Optimal Control—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
A Brief Overview of Unsupervised Neural Speech Representation Learning—0
ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval—0
Learning to Identify Physical Parameters from Video Using Differentiable Physics—0
Learning to Hash with Graph Neural Networks for Recommender Systems—0
Learning to Ground Multi-Agent Communication with Autoencoders—0
Multi-task Representation Learning with Stochastic Linear Bandits—0
DreamTeacher: Pretraining Image Backbones with Deep Generative Models—0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning—0
Learning-To-Embed: Adopting Transformer based models for E-commerce Products Representation Learning—0
Multi-Token Enhancing for Vision Representation Learning—0
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs—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