Ser Nam Lim

What makes fake images detectable? Understanding properties that generalize

The quality of image generation and manipulation is reaching impressive ...
08/24/2020 ∙ by Lucy Chai, et al. ∙ 41 ∙ share

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PyTorch Metric Learning

Deep metric learning algorithms have a wide variety of applications, but...
08/20/2020 ∙ by Kevin Musgrave, et al. ∙ 60 ∙ share

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MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA) aims to adapt models from a lab...
07/24/2020 ∙ by Luyu Yang, et al. ∙ 13 ∙ share

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Curriculum Manager for Source Selection in Multi-Source Domain Adaptation

The performance of Multi-Source Unsupervised Domain Adaptation depends s...
07/02/2020 ∙ by Luyu Yang, et al. ∙ 0 ∙ share

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Neural Manifold Ordinary Differential Equations

To better conform to data geometry, recent deep generative modelling tec...
06/18/2020 ∙ by Aaron Lou, et al. ∙ 25 ∙ share

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Detecting Deep-Fake Videos from Appearance and Behavior

Synthetically-generated audios and videos – so-called deep fakes – conti...
04/29/2020 ∙ by Shruti Agarwal, et al. ∙ 3 ∙ share

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Quantization Guided JPEG Artifact Correction

The JPEG image compression algorithm is the most popular method of image...
04/17/2020 ∙ by Max Ehrlich, et al. ∙ 10 ∙ share

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One-Shot Domain Adaptation For Face Generation

In this paper, we propose a framework capable of generating face images ...
03/28/2020 ∙ by Chao Yang, et al. ∙ 11 ∙ share

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A Metric Learning Reality Check

Deep metric learning papers from the past four years have consistently c...
03/18/2020 ∙ by Kevin Musgrave, et al. ∙ 85 ∙ share

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Set-Structured Latent Representations

Unstructured data often has latent component structure, such as the obje...
03/09/2020 ∙ by Qian Huang, et al. ∙ 14 ∙ share

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Deep Multi-Modal Sets

Many vision-related tasks benefit from reasoning over multiple modalitie...
03/03/2020 ∙ by Austin Reiter, et al. ∙ 27 ∙ share

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Differentiating through the Fréchet Mean

Recent advances in deep representation learning on Riemannian manifolds ...
02/29/2020 ∙ by Aaron Lou, et al. ∙ 23 ∙ share

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On Feature Normalization and Data Augmentation

Modern neural network training relies heavily on data augmentation for i...
02/25/2020 ∙ by Boyi Li, et al. ∙ 19 ∙ share

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Measuring Dataset Granularity

Despite the increasing visibility of fine-grained recognition in our fie...
12/21/2019 ∙ by Yin Cui, et al. ∙ 54 ∙ share

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Unconstrained Facial Expression Transfer using Style-based Generator

Facial expression transfer and reenactment has been an important researc...
12/12/2019 ∙ by Chao Yang, et al. ∙ 17 ∙ share

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Fine-grained Synthesis of Unrestricted Adversarial Examples

We propose a novel approach for generating unrestricted adversarial exam...
11/20/2019 ∙ by Omid Poursaeed, et al. ∙ 12 ∙ share

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Unsupervised Deep Metric Learning via Auxiliary Rotation Loss

Deep metric learning is an important area due to its applicability to ma...
11/16/2019 ∙ by Xuefei Cao, et al. ∙ 26 ∙ share

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Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

We present a systematic study of adversarial attacks on state-of-the-art...
10/31/2019 ∙ by Zuxuan Wu, et al. ∙ 73 ∙ share

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Cross-X Learning for Fine-Grained Visual Categorization

Recognizing objects from subcategories with very subtle differences rema...
09/10/2019 ∙ by Wei Luo, et al. ∙ 9 ∙ share

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Enhancing Adversarial Example Transferability with an Intermediate Level Attack

Neural networks are vulnerable to adversarial examples, malicious inputs...
07/23/2019 ∙ by Qian Huang, et al. ∙ 0 ∙ share

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An Analysis of Object Embeddings for Image Retrieval

We present an analysis of embeddings extracted from different pre-traine...
05/28/2019 ∙ by Bor-Chun Chen, et al. ∙ 0 ∙ share

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Adversarial Example Decomposition

Research has shown that widely used deep neural networks are vulnerable ...
12/04/2018 ∙ by Horace He, et al. ∙ 0 ∙ share

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Intermediate Level Adversarial Attack for Enhanced Transferability

Neural networks are vulnerable to adversarial examples, malicious inputs...
11/20/2018 ∙ by Qian Huang, et al. ∙ 0 ∙ share

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DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation

Harvesting dense pixel-level annotations to train deep neural networks f...
04/16/2018 ∙ by Zuxuan Wu, et al. ∙ 0 ∙ share

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Unsupervised Domain Adaptation for Semantic Segmentation with GANs

Visual Domain Adaptation is a problem of immense importance in computer ...
11/19/2017 ∙ by Swami Sankaranarayanan, et al. ∙ 0 ∙ share

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Regularizing deep networks using efficient layerwise adversarial training

Adversarial training has been shown to regularize deep neural networks i...
05/22/2017 ∙ by Swami Sankaranarayanan, et al. ∙ 0 ∙ share

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Self corrective Perturbations for Semantic Segmentation and Classification

Convolutional Neural Networks have been a subject of great importance ov...
03/23/2017 ∙ by Swami Sankaranarayanan, et al. ∙ 0 ∙ share

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A Reinforcement Learning Approach to the View Planning Problem

We present a Reinforcement Learning (RL) solution to the view planning p...
10/19/2016 ∙ by Mustafa Devrim Kaba, et al. ∙ 0 ∙ share

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Ser Nam Lim

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