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
Deep Partial Multi-View Learning—0
Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation—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
Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting—0
A Comparative Study for Unsupervised Network Representation Learning—0
No Representation Rules Them All in Category Discovery—0
Identifying Functional Brain Networks of Spatiotemporal Wide-Field Calcium Imaging Data via a Long Short-Term Memory Autoencoder—0
Identifying critical nodes in complex networks by graph representation learning—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
Multi-scale Unified Network for Image Classification—0
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning—0
Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System—0
Non-Stationary Representation Learning in Sequential Linear Bandits—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
Nonsymbolic Text Representation—0
Normalizing self-supervised learning for provably reliable Change Point Detection—0
Deep Neural Networks with Massive Learned Knowledge—0
Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation 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
Non-Parametric Representation Learning with Kernels—0
Multi-target Backdoor Attacks for Code Pre-trained Models—0
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials—0
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification—0
Identifiable Latent Polynomial Causal Models Through the Lens of Change—0
Identifiable Latent Neural Causal Models—0
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning—0
Multi-task Fusion for Efficient Panoptic-Part Segmentation—0
Identifiable Feature Learning for Spatial Data with Nonlinear ICA—0
Multi-Task Imitation Learning for Linear Dynamical Systems—0
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning—0
An Impossibility Theorem for Node Embedding—0
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment—0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
Non-Recursive Graph Convolutional Networks—0
Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding—0
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits—0
Multi-task Representation Learning with Stochastic Linear Bandits—0
Deep Neural Decision Forests—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
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News—0
Multi-timescale Representation Learning in LSTM Language Models—0
Multi-Token Enhancing for Vision Representation Learning—0
Unsupervised Representation Learning with Minimax Distance Measures—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