Search Results for author: Wan-Yi Lin

Found 9 papers, 2 papers with code

Defending Multimodal Fusion Models against Single-Source Adversaries

no code implementations CVPR 2021 Karren Yang, Wan-Yi Lin, Manash Barman, Filipe Condessa, Zico Kolter

Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information between modalities.

Action Recognition object-detection +2

Empirical robustification of pre-trained classifiers

no code implementations ICML Workshop AML 2021 Mohammad Sadegh Norouzzadeh, Wan-Yi Lin, Leonid Boytsov, Leslie Rice, huan zhang, Filipe Condessa, J Zico Kolter

Most pre-trained classifiers, though they may work extremely well on the domain they were trained upon, are not trained in a robust fashion, and therefore are sensitive to adversarial attacks.

Denoising Image Reconstruction +1

Certified robustness against physically-realizable patch attack via randomized cropping

no code implementations1 Jan 2021 Wan-Yi Lin, Fatemeh Sheikholeslami, Jinghao Shi, Leslie Rice, J Zico Kolter

Our method improves upon the current state of the art in defending against patch attacks on CIFAR10 and ImageNet, both in terms of certified accuracy and inference time.

Classification Crop Classification +2

Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts

1 code implementation17 Sep 2020 Cheng-Che Lee, Wan-Yi Lin, Yen-Ting Shih, Pei-Yi Patricia Kuo, Li Su

Its major difference from the traditional image style transfer problem is that the style information is provided by music rather than images.

Style Transfer

Noisy Batch Active Learning with Deterministic Annealing

1 code implementation27 Sep 2019 Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin

We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles.

Active Learning Denoising +1

Learning in Confusion: Batch Active Learning with Noisy Oracle

no code implementations25 Sep 2019 Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin

We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles.

Active Learning Denoising +1

DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets

no code implementations29 Aug 2019 Govind Rathore, Wan-Yi Lin, Ji Eun Kim

Autonomous driving requires various computer vision algorithms, such as object detection and tracking. Precisely-labeled datasets (i. e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects.

Autonomous Driving object-detection +1

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