This repository is the code for the following paper: Zhuoyuan Wu, Jian Zhang, Chong Mou. Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging. ICCV 2021. [PDF] Introduction Snapshot compressive imaging (SCI) aims to record three-dimensional signals via a two-dimensional camera. For the sake of building a fast and accurate SCI recovery algorithm, we incorporate the interpretability of model-based methods and the speed of learning-based ones and present a novel dense deep unfolding network (DUN) with 3D-CNN prior […]

TensorFlow implementation of “A Simple Baseline for Bayesian Uncertainty in Deep Learning” Concept Algorithm to utilize the SWAG [1]. Equation for the weight sampling from SWAG [1]. Results The red color and the blue color represent the initial state and current state respectively. Performance MNIST Method Accuracy Precision Recall F1-Score Final Epoch 0.99230 0.99231 0.99222 0.99226 Best Loss 0.99350 0.99350 0.99338 0.99344 SWAG (S = 30) 0.99310 0.99305

Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR 2017: https://arxiv.org/abs/1703.03130 . USAGE: For binary sentiment classification on imdb dataset run : python classification.py “binary” For multiclass classification on reuters dataset run : python classification.py “multiclass” You can change the model parameters in the model_params.json file Other tranining parameters like number of attention hops etc can be configured in the config.json file. If you want to use pretrained glove embeddings , set the use_embeddings parameter […]