Pinned Repositories
Adversarial-Distributional-Training
Adversarial Distributional Training (NeurIPS 2020)
Analytic-continual-learning
This repository will be posting analytic continual learning series, including Analytic Class-Incremental Learning (ACIL), Gaussian Kernel Embedded Analytic Learning (GKEAL), Dual-Stream Analytic Learning (DS-AL), etc.
auto-attack
Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
AWP
Codes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"
Causal-Adversarial-Instruments
Official PyTorch Implementation for "Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression" in CVPR 2023
CNN_Visualizations
Visualization of Adversarial Examples
consistent_depth
We estimate dense, flicker-free, geometrically consistent depth from monocular video, for example hand-held cell phone video.
deep-symbolic-optimization
Source code for deep symbolic optimization.
DF-Net
[ECCV 2018] DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
GMAA
tokaka22's Repositories
tokaka22/GMAA
tokaka22/Analytic-continual-learning
This repository will be posting analytic continual learning series, including Analytic Class-Incremental Learning (ACIL), Gaussian Kernel Embedded Analytic Learning (GKEAL), Dual-Stream Analytic Learning (DS-AL), etc.
tokaka22/auto-attack
Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
tokaka22/AWP
Codes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"
tokaka22/Causal-Adversarial-Instruments
Official PyTorch Implementation for "Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression" in CVPR 2023
tokaka22/consistent_depth
We estimate dense, flicker-free, geometrically consistent depth from monocular video, for example hand-held cell phone video.
tokaka22/deep-symbolic-optimization
Source code for deep symbolic optimization.
tokaka22/DiffPure
A new adversarial purification method that uses the forward and reverse processes of diffusion models to remove adversarial perturbations.
tokaka22/google-research
Google Research
tokaka22/Pyraformer
tokaka22/RAFT
tokaka22/DKL
Decoupled Kullback-Leibler Divergence Loss (DKL)
tokaka22/FSR
Feature Separation and Recalibration (CVPR 2023 Highlights)
tokaka22/InfoBatch
Lossless Training Speed Up by Unbiased Dynamic Data Pruning
tokaka22/LogitClip
ICML 2023 Mitigating memorization of noisy labels by clipping the model prediction
tokaka22/MAIR
PyTorch implementation of adversarial training and defenses [Fantastic Robustness Measures: The Secrets of Robust Generalization, NeurIPS 2023].
tokaka22/Meta-Pec
The implementation of Meta-Pec
tokaka22/MTARD
The Code of ECCV2022:Enhanced Accuracy and Robustness via Multi-Teacher Adversarial Distillation
tokaka22/PersonalizedFL
Personalized federated learning codebase for research
tokaka22/PREP-SHOT
Pathways for Renewable Energy Planning coupling Short-term Hydropower OperaTion
tokaka22/RDAT
tokaka22/SOD-CNNs-based-code-summary-
The summary of code and paper for salient object detection with deep learning
tokaka22/Time_Series_Backdoor_Attack
SaTML'23 paper "Backdoor Attacks on Time Series: A Generative Approach" by Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, and James Bailey.
tokaka22/torch-cam
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
tokaka22/TPAP
tokaka22/TransferAttack
TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.
tokaka22/ucasthesis
LaTeX Thesis Template for the University of Chinese Academy of Sciences
tokaka22/UPFlow_pytorch
PyTorch implementation of UPFlow (unsupervised optical flow learning)
tokaka22/Versatile-Weight-Attack
The implementatin of Versatile Weight Attack via Flipping Limited Bits, including Singel Sample Attack and Triggered Samples Attack.
tokaka22/VHL
ICML2022: Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning