← Learning Types
Machine Learning › Learning Types ›

Contrastive Learning

3535 directly classified papers

Papers per year

Papers

An Analysis of the Utility of Explicit Negative Examples to Improve the Syntactic Abilities of Neural Language Models ACL 2020
LoCo: Local Contrastive Representation Learning NIPS 2020
On Contrastive Learning for Likelihood-free Inference ICML 2020
Time-Consistent Self-Supervision for Semi-Supervised Learning ICML 2020
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models ACL 2020
Cycle-Contrast for Self-Supervised Video Representation Learning NIPS 2020
Debiased Contrastive Learning NIPS 2020
Contrastive Variational Reinforcement Learning for Complex Observations CORL 2020
Unsupervised Anomaly Detection in Parole Hearings using Language Models EMNLP 2020
Bi-Objective Continual Learning: Learning ‘New’ While Consolidating ‘Known’ AAAI 2020
Supervised Contrastive Learning NIPS 2020
Contrastive Learning with Adversarial Examples NIPS 2020
Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning EMNLP 2020
CoMIR: Contrastive Multimodal Image Representation for Registration NIPS 2020
Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering EMNLP 2020
Contrastive Predictive Coding of Audio with an Adversary INTERSPEECH 2020
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering CORL 2020
Robust Pre-Training by Adversarial Contrastive Learning NIPS 2020
Deep Reinforcement and InfoMax Learning NIPS 2020
Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation INTERSPEECH 2020
Distilling Structured Knowledge for Text-Based Relational Reasoning EMNLP 2020
Contrastive Distillation on Intermediate Representations for Language Model Compression EMNLP 2020
Adversarial Self-Supervised Contrastive Learning NIPS 2020
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding EMNLP 2020
Group-wise Contrastive Learning for Neural Dialogue Generation EMNLP 2020