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 6451–6500 of 10580 papers

TitleStatusHype
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis—0
Dropping Convexity for More Efficient and Scalable Online Multiview Learning—0
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Dropout Training for SVMs with Data Augmentation—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Multi-modal reward for visual relationships-based image captioning—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Learning Universal Representations from Word to Sentence—0
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
Multimodal sparse representation learning and applications—0
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning—0
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection—0
A Framework for Generalizing Graph-based Representation Learning Methods—0
Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting—0
Learning unbiased features—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Learning Treatment Representations for Downstream Instrumental Variable Regression—0
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers—0
Learning to See in the Dark with Events—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Learning to Represent Individual Differences for Choice Decision Making—0
Multi-Output Distributional Fairness via Post-Processing—0
DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning—0
Towards Causal Representation Learning and Deconfounding from Indefinite Data—0
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving—0
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features—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
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
CORAL: Concept Drift Representation Learning for Co-evolving Time-series—0
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy—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
Choose What You Need: Disentangled Representation Learning for Scene Text Recognition Removal and Editing—0
Multi-scale 2D Representation Learning for weakly-supervised moment retrieval—0
Learning to Learn with Conditional Class Dependencies—0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—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