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 2751–2800 of 10580 papers

TitleStatusHype
Automatic Generation of Chinese Handwriting via Fonts Style Representation Learning—0
ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations—0
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
FairGen: Towards Fair Graph Generation—0
Fair Inference for Discrete Latent Variable Models—0
Convexified Message-Passing Graph Neural Networks—0
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware—0
Automatic Data Visualization Generation from Chinese Natural Language Questions—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Adaptive Text Recognition through Visual Matching—0
Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Controlling Computation versus Quality for Neural Sequence Models—0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
Factorized Visual Tokenization and Generation—0
ControlVAE: Controllable Variational Autoencoder—0
Controllable Invariance through Adversarial Feature Learning—0
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning—0
Controllable Chest X-Ray Report Generation from Longitudinal Representations—0
Controllable Augmentations for Video Representation Learning—0
Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing—0
Control False Negative Instances In Contrastive Learning To ImproveLong-tailed Item Categorization—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Control-Aware Representations for Model-based Reinforcement Learning—0
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation—0
A Metric Learning Approach to Misogyny Categorization—0
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations—0
Factors of Transferability for a Generic ConvNet Representation—0
Fair Interpretable Learning via Correction Vectors—0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—0
Contrast Phase Classification with a Generative Adversarial Network—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training—0
IN-Sight: Interactive Navigation through Sight—0
Contrastive Video-Language Learning with Fine-grained Frame Sampling—0
Facial Expression Representation Learning by Synthesizing Expression Images—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
Contrastive Unsupervised Learning for Speech Emotion Recognition—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning—0
Factor Graph Neural Networks—0
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning—0
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning—0
Contrastive String Representation Learning using Synthetic Data—0
A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization—0
Contrastive Separative Coding for Self-supervised 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