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 8101–8150 of 10580 papers

TitleStatusHype
Knowledge Distillation Under Ideal Joint Classifier Assumption—0
Discourse-Aware Graph Networks for Textual Logical Reasoning—0
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph—0
DisCoRL: Continual Reinforcement Learning via Policy Distillation—0
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation—0
Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings—0
CapsNet for Medical Image Segmentation—0
Robust Representation Learning for Unreliable Partial Label Learning—0
Approximate Fiber Product: A Preliminary Algebraic-Geometric Perspective on Multimodal Embedding Alignment—0
Adversarial Representation Learning for Text-to-Image Matching—0
Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
CAPS: A Practical Partition Index for Filtered Similarity Search—0
Fine-grained Early Frequency Attention for Deep Speaker Representation Learning—0
DISC: Deep Image Saliency Computing via Progressive Representation Learning—0
Robust Speaker Recognition with Transformers Using wav2vec 2.0—0
Robust Speech Representation Learning via Flow-based Embedding Regularization—0
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval—0
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification—0
Disassembling Object Representations without Labels—0
EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments—0
Applying the Information Bottleneck Principle to Prosodic Representation Learning—0
XAI Beyond Classification: Interpretable Neural Clustering—0
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data—0
Robust Visual Imitation Learning with Inverse Dynamics Representations—0
Kinship Representation Learning with Face Componential Relation—0
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning—0
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation—0
KGNN: Distributed Framework for Graph Neural Knowledge Representation—0
DiRW: Path-Aware Digraph Learning for Heterophily—0
Application of Knowledge Distillation to Multi-task Speech Representation Learning—0
KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos—0
Rotation-Agnostic Image Representation Learning for Digital Pathology—0
Direction-Aware Hybrid Representation Learning for 3D Hand Pose and Shape Estimation—0
Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference—0
KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering—0
Directional Sign Loss: A Topology-Preserving Loss Function that Approximates the Sign of Finite Differences—0
Can Temporal Information Help with Contrastive Self-Supervised Learning?—0
R-Spin: Efficient Speaker and Noise-invariant Representation Learning with Acoustic Pieces—0
Kernel Transform Learning—0
Kernel Stochastic Configuration Networks for Nonlinear Regression—0
Directional Self-supervised Learning for Heavy Image Augmentations—0
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach—0
Directionally Convolutional Networks for 3D Shape Segmentation—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
S^2ALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning—0
KERMIT: Generative Insertion-Based Modeling for Sequences—0
Directional diffusion models for graph 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