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ai4oac2023

am207_project_deep_learning_uncertainties_calibration

calibrated_regression

A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.

cross-season-segmentation

Code for "A Cross-Season Correspondence Dataset for Robust Semantic Segmentation"

deep-generative-models

deeplabv3plus-pytorch

DeepLabv3, DeepLabv3+ and pretrained models on VOC & Cityscapes

edge-connect

EdgeConnect: Structure Guided Image Inpainting using Edge Prediction, ICCVW 2019 https://arxiv.org/abs/1901.00212

elemenets-of-causal-inference-solutions

Solutions of problems in Elements of Causal Inerence book

elements_of_causal_inference_solutions

gp-gan

Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

gtsfm

End-to-end SFM framework based on GTSAM

hierarchy-image-retrieval

Code for the paper "Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval" published in ICVS 2023

image-blending-using-gp-gans

Implementation of GP-GAN: Towards Realistic High-Resolution Image Blending using PyTorch

maple-ude

maple-uncertainty-estimation

Code for the ICCV 2023 Workshop paper "Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers"

pix2pix

Image-to-image translation with conditional adversarial nets

pix2pixhd

Synthesizing and manipulating 2048x1024 images with conditional GANs

prml

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

prml_python

Implement prml contents with python

pytorch-cyclegan-and-pix2pix

Image-to-Image Translation in PyTorch

rotate-bb

self-attention-cv

Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.

spade

Semantic Image Synthesis with SPADE

spatule-tags-tf2-object-detection-api

steal

STEAL - Learning Semantic Boundaries from Noisy Annotations

synthetic-data-generator

Code for the paper "Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach" published in the journal Engineering Applications of Artificial Intelligence

tree-barks-generation

Code for the paper "A data driven approach to generate realistic 3D tree barks" published in Graphical Models

vaishwarya96.github.io

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