Search Results for author: Kibok Lee

Found 14 papers, 7 papers with code

Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark

no code implementations22 Jul 2022 Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer

Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain.

Few-Shot Learning Few-Shot Object Detection +1

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

1 code implementation NeurIPS 2021 Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin

Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random cropping and color jittering.

Representation Learning Transfer Learning

ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds

no code implementations24 May 2020 Kibok Lee, Zhuoyuan Chen, Xinchen Yan, Raquel Urtasun, Ersin Yumer

Our shape-aware adversarial attacks are orthogonal to existing point cloud based attacks and shed light on the vulnerability of 3D deep neural networks.

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

2 code implementations ICLR 2020 Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee

Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images.

Data Augmentation reinforcement-learning

Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks

no code implementations ICLR 2019 Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin

For instance, on CIFAR-10 dataset containing 45% noisy training labels, we improve the test accuracy of a deep model optimized by the state-of-the-art noise-handling training method from33. 34% to 43. 02%.

Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild

1 code implementation ICCV 2019 Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee

Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task.

class-incremental learning Incremental Learning

Robust Inference via Generative Classifiers for Handling Noisy Labels

1 code implementation31 Jan 2019 Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin

Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets.

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

4 code implementations NeurIPS 2018 Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin

Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications.

class-incremental learning Incremental Learning +1

Hierarchical Novelty Detection for Visual Object Recognition

no code implementations CVPR 2018 Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee

The essential ingredients of our methods are confidence-calibrated classifiers, data relabeling, and the leave-one-out strategy for modeling novel classes under the hierarchical taxonomy.

Generalized Zero-Shot Learning Object Recognition

Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

3 code implementations ICLR 2018 Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin

The problem of detecting whether a test sample is from in-distribution (i. e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications.

Towards Understanding the Invertibility of Convolutional Neural Networks

no code implementations24 May 2017 Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee

Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible.

Compressive Sensing General Classification

Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification

no code implementations21 Jun 2016 Yuting Zhang, Kibok Lee, Honglak Lee

Inspired by the recent trend toward revisiting the importance of unsupervised learning, we investigate joint supervised and unsupervised learning in a large-scale setting by augmenting existing neural networks with decoding pathways for reconstruction.

General Classification Image Classification

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