Contrastive Feature

Contrastive feature learning is a machine learning technique focused on enhancing feature representations by comparing and contrasting data points. Current research emphasizes applications across diverse fields, including image retrieval, anomaly detection, and domain adaptation, often employing contrastive loss functions within deep learning architectures like diffusion models and Siamese networks. This approach improves model performance by learning more discriminative features, leading to advancements in areas such as medical image analysis, natural language processing, and computer vision tasks where robust feature representation is crucial. The resulting improvements in accuracy and efficiency have significant implications for various applications, from automated disease diagnosis to improved search algorithms.

Papers

November 14, 2024

Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment
BoHao Chen
Contrastive Learning Self Supervised Learning Meta Learning Alignment Problem Latent Feature Contrastive Feature Incomplete Multi View Data

January 11, 2024

ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration
David Rivas-Villar, Álvaro S. Hervella, José Rouco, Jorge Novo
Contrastive Learning Multi View Triplet Loss Contrastive Feature Hand Crafted Descriptor

December 26, 2023

Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection
Songmin Dai, Yifan Wu, Xiaoqiang Li, Xiangyang Xue
Unsupervised Anomaly Detection Anomaly Sample Contrastive Feature Data Reweighting

December 13, 2023

C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation
Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
Aerial Image Cross View Aerial Imagery Contrastive Feature

November 15, 2023

Contrastive Chain-of-Thought Prompting
Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, Lidong Bing
Chain of Thought Prompting Language Model Reasoning Contrastive Feature Contrastive Demonstration

October 27, 2023

Feature Selection in the Contrastive Analysis Setting
Ethan Weinberger, Ian Covert, Su-In Lee
Representation Learning Feature Selection Contrastive Objective Biomedical Datasets Contrastive Feature

October 8, 2023

Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT
Akshaj Kumar Veldanda, Fabian Grob, Shailja Thakur, Hammond Pearce, Benjamin Tan, Ramesh Karri, Siddharth Garg
ChatGPT Generated Conversation Absolute Stance Bias Resume Job Contrastive Feature

July 17, 2023

Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training
Nathaniel Berger, Miriam Exel, Matthias Huck, Stefan Riezler
Neural Machine Translation Supervised Learning Maximum Likelihood K TOKEN Contrastive Feature

June 15, 2023

Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation
Tianyu Li, Subhankar Roy, Huayi Zhou, Hongtao Lu, Stephane Lathuiliere
Semantic Segmentation Feature Alignment Domain Adaptive Semantic Segmentation unsupeRvised Contrastive Learning Identity Stylization Pixel Level Alignment Contrastive Feature Pixel Contrast

May 22, 2023

Evaluating Pragmatic Abilities of Image Captioners on A3DS
Polina Tsvilodub, Michael Franke
Image Captioning Contrastive Feature Contrastive Captioners Natural Language Annotation Pragmatic Competence Language Model Performance

April 7, 2023

Linking Representations with Multimodal Contrastive Learning
Abhishek Arora, Xinmei Yang, Shao-Yu Jheng, Melissa Dell
Contrastive Learning Meaningful Representation Multimodal Learning Contrastive Feature Multimodal Embeddings Record Linkage

January 11, 2023

Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Robbie Holland, Oliver Leingang, Christopher Holmes, Philipp Anders, Rebecca Kaye, Sophie Riedl, Johannes C. Paetzold, Ivan Ezhov, Hrvoje Bogunović, Ursula Schmidt-Erfurth, Lars Fritsche, Hendrik P. N. Scholl, Sobha Sivaprasad, Andrew J. Lotery, Daniel Rueckert, Martin J. Menten
Disease Progression Age Related Macular Degeneration Contrastive Feature Disease Trajectory

October 5, 2022

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, Bihan Wen, Alan L. Yuille, Zongwei Zhou
Contrastive Learning Active Learning Value Laden Choice Active Vision Efficient Annotation Contrastive Feature Cold Start Problem

September 28, 2022

Learning Deep Representations via Contrastive Learning for Instance Retrieval
Tao Wu, Tie Luo, Donald Wunsch
Contrastive Learning Transfer Learning Deep Representation Instance Level Contrastive Feature

August 12, 2022

Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation
Georgios Kouros, Shubham Shrivastava, Cédric Picron, Sushruth Nagesh, Punarjay Chakravarty, Tinne Tuytelaars
Pose Estimation Human Pose Category Level Contrastive Feature Occlusion Augmentation

April 4, 2022

Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations
Manuel Pérez-Carrasco, Pavlos Protopapas, Guillermo Cabrera-Vives
Domain Adaptation Semi Supervised Learning Strong Consistency Feature Representation Semi Supervised Domain Adaptation Contrastive Feature

March 14, 2022

Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification
Haotian Wu, Anyu Zhang, Zeyu Cao
Contrastive Learning Self Supervised Hyperspectral Image Classification Hyperspectral Data Contrastive Feature Cross Representation Cross View Prediction

March 7, 2022

Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
Alvaro Gomariz, Huanxiang Lu, Yun Yvonna Li, Thomas Albrecht, Andreas Maunz, Fethallah Benmansour, Alessandra M. Valcarcel, Jennifer Luu, Daniela Ferrara, Orcun Goksel
Contrastive Learning Domain Adaptation Semi Supervised Learning Optical Coherence Tomography Accurate Segmentation Contrastive Feature

November 3, 2021

Video Salient Object Detection via Contrastive Features and Attention Modules
Yi-Wen Chen, Xiaojie Jin, Xiaohui Shen, Ming-Hsuan Yang
Contrastive Loss Attention Module Contrastive Feature Video Salient Object Detection Non Local Attention

Contrastive Feature

Papers

Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment

ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration

Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection

C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation

Contrastive Chain-of-Thought Prompting

Feature Selection in the Contrastive Analysis Setting

Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT

Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training

Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation

Evaluating Pragmatic Abilities of Image Captioners on A3DS

Linking Representations with Multimodal Contrastive Learning

Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning

Learning Deep Representations via Contrastive Learning for Instance Retrieval

Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation

Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations

Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification

Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation

Video Salient Object Detection via Contrastive Features and Attention Modules