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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9251–9300 of 10580 papers

TitleStatusHype
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification—0
Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation—0
Discovering Traveling Companions using Autoencoders—0
Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer—0
Discovery and Separation of Features for Invariant Representation Learning—0
Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
Discrete Infomax Codes for Supervised Representation Learning—0
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck—0
Discriminability Distillation in Group Representation Learning—0
Discriminability Distillation in Group Representation Learning—0
Discriminability-enforcing loss to improve representation learning—0
Discrimination-Aware Mechanism for Fine-Grained Representation Learning—0
Discriminative Autoencoder for Feature Extraction: Application to Character Recognition—0
Discriminative Block-Diagonal Representation Learning for Image Recognition—0
Discriminative Covariance Oriented Representation Learning for Face Recognition With Image Sets—0
Discriminative Cross-View Binary Representation Learning—0
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection—0
VladVA: Discriminative Fine-tuning of LVLMs—0
Discriminative-Generative Representation Learning for One-Class Anomaly Detection—0
Discriminative Graph Autoencoder—0
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Discriminative Video Representation Learning Using Support Vector Classifiers—0
Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest—0
Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement—0
Disentangled and Robust Representation Learning for Bragging Classification in Social Media—0
Disentangled Code Representation Learning for Multiple Programming Languages—0
Disentangled Face Representations in Deep Generative Models and the Human Brain—0
Disentangled Feature Learning for Real-Time Neural Speech Coding—0
Disentangled Generation with Information Bottleneck for Few-Shot Learning—0
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—0
Disentangled Generative Graph Representation Learning—0
Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs—0
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference—0
Disentangled Recurrent Wasserstein Autoencoder—0
Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective—0
Disentangled Representation Learning—0
Disentangled Representation Learning and Generation with Manifold Optimization—0
Disentangled representation learning for multilingual speaker recognition—0
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Disentangled Representation Learning for Controllable Person Image Generation—0
Disentangled Representation Learning for Parametric Partial Differential Equations—0
Disentangled Representation Learning for Causal Inference with Instruments—0
Pretrained Reversible Generation as Unsupervised Visual Representation Learning—0
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition—0
Disentangled Representation Learning with the Gromov-Monge Gap—0
Disentangled Representation Learning Using (β-)VAE and GAN—0
Disentangled Representation Learning with Information Maximizing Autoencoder—0
Disentangled Representation Learning with Sequential Residual Variational Autoencoder—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