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Representation Learning

501 papers with code ยท Methodology

Representation learning is concerned with training machine learning algorithms to learn useful representations, e.g. those that are interpretable, have latent features, or can be used for transfer learning.

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Latest papers without code

Continuous Histogram Loss: Beyond Neural Similarity

6 Apr 2020

Similarity learning has gained a lot of attention from researches in recent years and tons of successful approaches have been recently proposed.

REPRESENTATION LEARNING

Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning

6 Apr 2020

In this study, Proximal Policy Optimization (PPO) algorithm is augmented with Generative Adversarial Networks (GANs) to increase the sample efficiency by enforcing the network to learn efficient representations without depending on sparse and delayed rewards as supervision.

ATARI GAMES REPRESENTATION LEARNING ROBOT NAVIGATION

Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

6 Apr 2020

Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries.

KNOWLEDGE GRAPHS LINK PREDICTION REPRESENTATION LEARNING

Clustering based Contrastive Learning for Improving Face Representations

5 Apr 2020

We demonstrate our method on the challenging task of learning representations for video face clustering.

REPRESENTATION LEARNING

Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation

5 Apr 2020

We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.

3D POSE ESTIMATION REPRESENTATION LEARNING

FairNN- Conjoint Learning of Fair Representations for Fair Decisions

5 Apr 2020

In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning.

DECISION MAKING REPRESENTATION LEARNING

Adversarial-Prediction Guided Multi-task Adaptation for Semantic Segmentation of Electron Microscopy Images

5 Apr 2020

To complicate matters further, supervised learning models may not generalize well on a novel dataset due to domain shift.

REPRESENTATION LEARNING SEMANTIC SEGMENTATION

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip

3 Apr 2020

In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme.

REPRESENTATION LEARNING

Disassembling Object Representations without Labels

3 Apr 2020

In this paper, we study a new representation-learning task, which we termed as disassembling object representations.

REPRESENTATION LEARNING ZERO-SHOT LEARNING