jhkim0911

Accessing-and-modifying-different-layers-of-a-pretrained-model-in-pytorch

AdvancedML

Reading list for the Advanced Machine Learning Course

Adversarial-Information-Bottleneck

Official PyTorch Implementation for Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)

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

Causal-Unsupervised-Segmentation

Official PyTorch Implementation code for realizing the technical part of Causal Unsupervised Semantic sEgmentation (CAUSE) to improve performance of unsupervised semantic segmentation. (Under Review)

Data-Science--Cheat-Sheet

Cheat Sheets

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

DeepExplain

A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)

jhkim0911

Robust-Perturbation

PyTorch Implementation for Robust Perturbation for Visual Explanation: Cross-Checking Mask Optimization to Avoid Class Distortion

jhkim0911/Robust-Perturbation

PyTorch Implementation for Robust Perturbation for Visual Explanation: Cross-Checking Mask Optimization to Avoid Class Distortion

jhkim0911/jhkim0911

jhkim0911/jhkim0911.github.io

A simple and elegant Jekyll theme for an academic personal homepage

jhkim0911/Causal-Unsupervised-Segmentation

Official PyTorch Implementation code for realizing the technical part of Causal Unsupervised Semantic sEgmentation (CAUSE) to improve performance of unsupervised semantic segmentation. (Under Review)

jhkim0911/AdvancedML

Reading list for the Advanced Machine Learning Course

jhkim0911/Adversarial-Information-Bottleneck

Official PyTorch Implementation for Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)

jhkim0911/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

jhkim0911/Data-Science--Cheat-Sheet

Cheat Sheets

jhkim0911/deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

jhkim0911/DeepExplain

A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)

jhkim0911/disentangled-representation-papers

A curated list of research papers related to learning disentangled representations

jhkim0911/Double-Debiased-Adversary

Official PyTorch Implementation for "Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning" in ICCV 2023

jhkim0911/Feature-visualization

Deep learning CNN feature visualization using PyTorch

jhkim0911/fewshot-CAN

jhkim0911/IBA-paper-code

Code for the Paper "Restricting the Flow: Information Bottlenecks for Attribution"

jhkim0911/image-to-image-papers

A collection of image to image papers with code (constantly updating)

jhkim0911/input-switcher

Switch inputs with hidapitester (Windows & Linux)

jhkim0911/invert_MV_pytorch

jhkim0911/knockknock

🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

jhkim0911/LLMs-from-scratch

Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step

jhkim0911/lucid

A collection of infrastructure and tools for research in neural network interpretability.

jhkim0911/Masking-Adversarial-Damage

Official PyTorch Implementation for "Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network" in CVPR22

jhkim0911/numerical-linear-algebra

Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

jhkim0911/PathwayGrad

Code to reproduce the results of the paper: A Khakzar, S Baselizadeh, S Khanduja, C Rupprecht, ST Kim, N Navab, Neural Response Interpretation through the Lens of Critical Pathways, CVPR 2021.

jhkim0911/pytorch-cnn-visualizations

Pytorch implementation of convolutional neural network visualization techniques

jhkim0911/pytorch_iCNN

jhkim0911/SEGAN

w Korean speech data

jhkim0911/TTUR

Two time-scale update rule for training GANs

jhkim0911/XAI-papers

jhkim0911/XCAD2021