FLAT[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory
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causal-semantic-generative-modelCodes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)
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Mutual labels: generative-model
pytorch-CycleGANPytorch implementation of CycleGAN.
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Mutual labels: generative-model
PGD-pytorchA pytorch implementation of "Towards Deep Learning Models Resistant to Adversarial Attacks"
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favorite-research-papersListing my favorite research papers 📝 from different fields as I read them.
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style-vaeImplementation of VAE and Style-GAN Architecture Achieving State of the Art Reconstruction
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Lr-LiVAETensorflow implementation of Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions (CVPR 2019)
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Mutual labels: generative-model
GrabNetGrabNet: A Generative model to generate realistic 3D hands grasping unseen objects (ECCV2020)
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Mutual labels: generative-model
ShapeFormerOfficial repository for the ShapeFormer Project
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MidiTokA convenient MIDI / symbolic music tokenizer for Deep Learning networks, with multiple strategies 🎶
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adaptive-f-divergenceA tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"
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Generalization-Causality关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记
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AWPCodes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"
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py-msa-kdenlivePython script to load a Kdenlive (OSS NLE video editor) project file, and conform the edit on video or numpy arrays.
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procedural-advmlTask-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)
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TIGERPython toolbox to evaluate graph vulnerability and robustness (CIKM 2021)
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gcWGANGuided Conditional Wasserstein GAN for De Novo Protein Design
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Mutual labels: generative-model
sparse-rsSparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
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adversarial-recommender-systems-surveyThe goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-…
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