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 6801–6850 of 10580 papers

TitleStatusHype
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network—0
Nonparametric Canonical Correlation Analysis—0
Nonparametric Factor Analysis and Beyond—0
Unsupervised Representation Learning with Minimax Distance Measures—0
Learning Representations from Dendrograms—0
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning—0
Learning Representations from Audio-Visual Spatial Alignment—0
Learning Representations for Pixel-based Control: What Matters and Why?—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning—0
Learning Representations for Incomplete Time Series Clustering—0
Non-stationary Domain Generalization: Theory and Algorithm—0
Learning Representations for Detecting Abusive Language—0
Nonsymbolic Text Representation—0
Domain Adaptive Graph Classification—0
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning—0
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition—0
NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC—0
Learning Representations by Humans, for Humans—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism—0
NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Nuclear Norm Regularization for Deep Learning—0
Learning Relational Representations with Auto-encoding Logic Programs—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning—0
Learning Hierarchical Protein Representations via Complete 3D Graph Networks—0
Learning Progressive Point Embeddings for 3D Point Cloud Generation—0
Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Artificial Intelligence Augmented Medical Imaging Reconstruction in Radiation Therapy—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—0
Object-Centric Representation Learning from Unlabeled Videos—0
Object-Centric Representation Learning with Generative Spatial-Temporal Factorization—0
ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs—0
Activation Map Adaptation for Effective Knowledge Distillation—0
Object-Level Representation Learning for Few-Shot Image Classification—0
Learning Private Representations with Focal Entropy—0
Learning Policy Representations in Multiagent Systems—0
ObPose: Leveraging Pose for Object-Centric Scene Inference and Generation in 3D—0
ChainNet: Learning on Blockchain Graphs with Topological Features—0
LEARNING PHONEME-LEVEL DISCRETE SPEECH REPRESENTATION WITH WORD-LEVEL SUPERVISION—0
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network—0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—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