Official-PyTorch-Implementation-of-TransMEF
Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework via Self-Supervised Multi-Task Learning. Code will be available soon.
Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework via Self-Supervised Multi-Task Learning. Code will be available soon.
Overview
Official repository of the paper "A Variational Approximation for Analyzing the Dynamics of Panel Data". Mixed Effect Neural ODE. UAI 2021.
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Image-Stitching - Panorama composition using SIFT Features and a custom implementaion of RANSAC algorithm
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adversarial_multi_armed_bandit_variable_plays
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A PyTorch implementation of DenseNet.
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Behavioral "black-box" testing for recommender systems
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Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.
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Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021
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Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.
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3.8% and 18.3% on CIFAR-10 and CIFAR-100
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Trustworthy AI related projects
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Selective Wavelet Attention Learning for Single Image Deraining
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This repository is a series of notebooks that show solutions for the projects at Dataquest.io.
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A rough implementation of the paper "A Steering Algorithm for Redirected Walking Using Reinforcement Learning"
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Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach
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[ICCV 2021] Group-aware Contrastive Regression for Action Quality Assessment
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Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)
Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a
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