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Youngmin Ro

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4 papers

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AAAI Conference 2022 Conference Paper

Differentially Private Normalizing Flows for Synthetic Tabular Data Generation

Jaewoo Lee
Minjung Kim
Yonghyun Jeong
Youngmin Ro

Normalizing flows have shown to be a promising approach to deep generative modeling due to their ability to exactly evaluate density — other alternatives either implicitly model the density or use approximate surrogate density. In this work, we present a differentially private normalizing flow model for heterogeneous tabular data. Normalizing flows are in general not amenable to differentially private training because they require complex neural networks with larger depth (compared to other generative models) and use specialized architectures for which per-example gradient computation is difficult (or unknown). To reduce the parameter complexity, the proposed model introduces a conditional spline flow which simulates transformations at different stages depending on additional input and is shared among sub-flows. For privacy, we introduce two fine-grained gradient clipping strategies that provide a better signal-to-noise ratio and derive fast gradient clipping methods for layers with custom parameterization. Our empirical evaluations show that the proposed model preserves statistical properties of original dataset better than other baselines.

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AAAI Conference 2022 Conference Paper

FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations

Yonghyun Jeong
Doyeon Kim
Youngmin Ro
Jongwon Choi

Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and the previous studies to originate from the frequency-level artifacts in generated images. We find that ignoring the frequency-level artifacts can improve the detector’s generalization across various GAN models, but it can reduce the model’s performance for the trained GAN models. Thus, we design a framework to generalize the deepfake detector for both the known and unseen GAN models. Our framework generates the frequency-level perturbation maps to make the generated images indistinguishable from the real images. By updating the deepfake detector along with the training of the perturbation generator, our model is trained to detect the frequency-level artifacts at the initial iterations and consider the image-level irregularities at the last iterations. For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories. Numerous experiments validate the state-of-the-art performance of our deepfake detector.

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AAAI Conference 2021 Conference Paper

AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks

Youngmin Ro
Jin Young Choi

Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layerwise weight variations by fine-tuning using a single learning rate do not match the well-known notion that lowerlevel layers extract general features and higher-level layers extract specific features. Based on our discussion, we propose an algorithm that improves fine-tuning performance and reduces network complexity through layer-wise pruning and auto-tuning of layer-wise learning rates. The proposed algorithm has verified the effectiveness by achieving state-of-the-art performance on the image retrieval benchmark datasets (CUB-200, Cars-196, Stanford online product, and Inshop). Code is available at https: //github. com/ youngminPIL/AutoLR.

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AAAI Conference 2019 Conference Paper

Backbone Cannot Be Trained at Once: Rolling Back to Pre-Trained Network for Person Re-Identification

Youngmin Ro
Jongwon Choi
Dae Ung Jo
Byeongho Heo
Jongin Lim
Jin Young Choi

In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a large dataset. However, it is relatively difficult to sufficiently finetune the low-level layers of the network due to the gradient vanishing problem. In this work, we propose a novel fine-tuning strategy that allows low-level layers to be sufficiently trained by rolling back the weights of high-level layers to their initial pre-trained weights. Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks. The improved performance of the proposed strategy is validated via several experiments. Furthermore, without any addons such as pose estimation or segmentation, our strategy exhibits state-of-the-art performance using only vanilla deep convolutional neural network architecture.

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