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11/20/2022
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
Large pre-trained models decay over long-term deployment as input distri...
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03/31/2022
Visual Prompting: Modifying Pixel Space to Adapt Pre-trained Models
Prompting has recently become a popular paradigm for adapting language m...
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02/10/2019
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
The predictive performance of supervised learning algorithms depends on ...
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04/03/2018
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
In recent years, the performance of face verification and recognition sy...
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12/03/2017
Improving Network Robustness against Adversarial Attacks with Compact Convolution
Though Convolutional Neural Networks (CNNs) have surpassed human-level p...
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11/19/2017
Unsupervised Domain Adaptation for Semantic Segmentation with GANs
Visual Domain Adaptation is a problem of immense importance in computer ...
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05/22/2017
Regularizing deep networks using efficient layerwise adversarial training
Adversarial training has been shown to regularize deep neural networks i...
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04/06/2017
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Domain Adaptation is an actively researched problem in Computer Vision. ...
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03/23/2017
Self corrective Perturbations for Semantic Segmentation and Classification
Convolutional Neural Networks have been a subject of great importance ov...
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11/06/2016
Deep Convolutional Neural Network Features and the Original Image
Face recognition algorithms based on deep convolutional neural networks ...
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11/03/2016
An All-In-One Convolutional Neural Network for Face Analysis
We present a multi-purpose algorithm for simultaneous face detection, fa...
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04/19/2016
Triplet Probabilistic Embedding for Face Verification and Clustering
Despite significant progress made over the past twenty five years, uncon...
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02/10/2016
Triplet Similarity Embedding for Face Verification
In this work, we present an unconstrained face verification algorithm an...
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01/28/2016
