repo stringlengths 8 116 | tasks stringlengths 8 117 | titles stringlengths 17 302 | dependencies stringlengths 5 372k | readme stringlengths 5 4.26k | __index_level_0__ int64 0 4.36k |
|---|---|---|---|---|---|
0bserver07/One-Hundred-Layers-Tiramisu | ['semantic segmentation'] | ['The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation'] | camvid_data_loader.py helper.py fc-densenet-model.py model-dynamic.py model-tiramasu-103.py model-tiramasu-67-func-api.py model-tiramasu-56.py train-tiramisu.py model-tiramasu-67.py load_data Tiramisu normalized one_hot_it Tiramisu Tiramisu Tiramisu Tiramisu Tiramisu step_decay rollaxis print len normalized append rang... | 0bserver07/One-Hundred-Layers-Tiramisu | 0 |
101vinayak/Neural-Style-Transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | images2gif.py checkImages writeGif get_cKDTree readGif NeuQuant GifWriter intToBin append uint8 astype copy int int checkImages hasattr handleSubRectangles GifWriter writeGifToFile convertImagesToPIL open fromarray asarray seek tell convert append enumerate open | # Neural-Style-Transfer Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image. Imple... | 1 |
12kleingordon34/NLP_masters_project | ['word embeddings'] | ['Gender Bias in Contextualized Word Embeddings'] | process_winogender_data.py main process_wino_data process_occ_stats float int list items replace append process_wino_data process_occ_stats | # NLP_masters_project Code base used for NLP project 2020. By Daniel de Vassimon Manela, Boris van Breugel, Tom Fisher, David Errington --- ## Contents * `process_ontonotes.ipynb`: Loads the Ontonotes Release 5.0 data from [Github](https://github.com/yuchenlin/OntoNotes-5.0-NER-BIO.git), and processes raw data into a s... | 2 |
131250208/TPlinker-joint-extraction | ['relation extraction'] | ['TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking'] | tplinker_plus/config.py common/utils.py tplinker/config.py tplinker/train.py tplinker_plus/train.py setup.py common/components.py tplinker/tplinker.py preprocess/__init__.py tplinker_plus/tplinker_plus.py LayerNorm HandshakingKernel DefaultLogger Preprocessor DataMaker4BiLSTM TPLinkerBert HandshakingTaggingScheme DataM... | # TPLinker **TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking** This repository contains all the code of the official implementation for the paper: **[TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://www.aclweb.org/antholog... | 3 |
15saurabh16/Multipoles | ['time series'] | ['Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks'] | COMET_fMRI_Multiple_Parameters.py COMET_Climate.py bronk_kerb.py empirical_observations.py SignifTesterMultipole.py quick-cliques/utils/edges2snap.py quick-cliques/utils/invertdimacs.py Misc_Modules.py get_graph_in_txt.py COMET_ADVANCED.py RandomSearch_Climate.py COMET_fMRI.py quick-cliques/utils/edge2dimacs.py quick-c... | # Multipoles This repository contains the code that we used to find "multipoles", a new class of multivariate relationship patterns in time series data in datasets from climate and neuroscience domain. See [1],[2] for further details. <b>Instructions to run the code:</b> 1) The code to obtain all the empirical observa... | 4 |
1980x/ABAW2020DMACS | ['facial expression recognition'] | ['Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information'] | dataset/affectwild2_dataset.py models/losses.py test_affwild2.py train_affwild2_expw_affectnet.py util.py dataset/sampler.py train_affwild2.py dataset/affectwild2_expw_affectnet.py models/attentionnet.py models/resnet.py validate statistic AverageMeter accuracy save_checkpoint adjust_learning_rate main train val_accura... | # ABAW2020 DMACS SSSIHL <strong>This is code for our submission in the expression track of ABAW 2020 competition.</strong> <strong> Results: https://ibug.doc.ic.ac.uk/resources/fg-2020-competition-affective-behavior-analysis/ </strong> Link to Presentation in FG-2020: https://drive.google.com/file/d/1loxCTklHu5hhkA_3pq... | 5 |
1980x/SCAN-CCI-FER | ['facial expression recognition'] | ['Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition', 'Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information'] | GACNN/main_sfew.py dataset/ckplus_dataset_cv.py dataset/affectnet_dataset.py main_sfew.py main_oulucasia.py dataset/sampler.py GACNN/main_fplus.py utils/util.py OADN/models/resnet.py dataset/ferplus_dataset.py GACNN/ferplus_dataset.py OADN/train_ferplus.py OADN/dataset/rafdb_dataset_attentionmaps.py OADN/train_sfew.py ... | Our work has been published in Pattern Recognition letters. https://authors.elsevier.com/a/1ca-7cAmylz0f https://www.sciencedirect.com/science/article/abs/pii/S0167865521000489#absh0002 <strong>Title:</strong> Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expr... | 6 |
198808xc/OrganSegC2F | ['pancreas segmentation'] | ['A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans'] | OrganSegC2F/init.py OrganSegC2F/DataF.py OrganSegC2F/utils.py OrganSegC2F/fine_surgery.py OrganSegC2F/coarse_training.py OrganSegC2F/coarse_surgery.py OrganSegC2F/oracle_testing.py OrganSegC2F/coarse_fusion.py DATA2NPY/nii2npy.py OrganSegC2F/coarse_testing.py DATA2NPY/dicom2npy.py OrganSegC2F/fine_training.py OrganSegC... | # OrganSegC2F: a coarse-to-fine organ segmentation framework version 1.11 - Dec 3 2017 - by Yuyin Zhou and Lingxi Xie ### Please note: an improved version of OrganSegC2F named OrganSegRSTN is available: https://github.com/198808xc/OrganSegRSTN It outperforms OrganSegC2F by 84.50% vs. 82.37% on the NIH pancreas segmenta... | 7 |
198808xc/OrganSegRSTN | ['pancreas segmentation'] | ['Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation', 'A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans'] | OrganSegRSTN/surgery.py OrganSegRSTN/Crop.py OrganSegRSTN/coarse2fine_testing.py OrganSegRSTN/oracle_fusion.py OrganSegRSTN/Uncrop.py OrganSegRSTN/coarse_fusion.py OrganSegRSTN/Crop_old.py OrganSegRSTN/fast_functions.py OrganSegRSTN/indiv_training.py OrganSegRSTN/init.py OrganSegRSTN/coarse_testing.py OrganSegRSTN/Data... | # OrganSegRSTN: an end-to-end coarse-to-fine organ segmentation framework version 2.0 - Jul 31 2018 - by Qihang Yu, Yuyin Zhou and Lingxi Xie ### NOTEs: #### 1. v2.0 is a MAJOR update to v1.0, which we: (1) slightly changed network architecture (score layers are removed and change to a saliency layer), so that networ... | 8 |
1989Ryan/Semantic_SLAM | ['semantic slam', 'semantic segmentation', 'autonomous driving'] | ['Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment'] | Third_Part/PSPNet_Keras_tensorflow/train.py Third_Part/PSPNet_Keras_tensorflow/caffe-tensorflow/convert.py Third_Part/PSPNet_Keras_tensorflow/ade20k_labels.py Third_Part/PSPNet_Keras_tensorflow/python_utils/callbacks.py Third_Part/PSPNet_Keras_tensorflow/caffe-tensorflow/examples/imagenet/models/caffenet.py Third_Part/... | # Semantic SLAM  This on-going project is Semantic SLAM using ROS, ORB SLAM and PSPNet101. It will be used in autonomous robotics for semantic understanding and navigation. Now the visualized semantic map with topological information is reachable, wher... | 9 |
1Reinier/Reservoir | ['time series'] | ['Efficient Optimization of Echo State Networks for Time Series Datasets'] | docs/conf.py reservoir/detail/robustgpmodel.py reservoir/esn.py setup.py reservoir/esn_cv.py reservoir/scr.py tests/test_esn.py reservoir/__init__.py reservoir/clustering.py reservoir/detail/esn_bo.py setup_package ClusteringBO EchoStateNetwork EchoStateNetworkCV generate_states_inner_loop SimpleCycleReservoir EchoStat... | Reservoir ========= A Python 3 toolset for creating and optimizing Echo State Networks. >Author: Jacob Reinier Maat, Nikos Gianniotis >License: MIT >2016-2019 Contains: - Vanilla ESN and Simple Cyclic Reservoir architectures. - Bayesian Optimization with optimized routines for Echo State Nets through `GPy`. - Clu... | 10 |
201518018629031/HGATRD | ['graph attention'] | ['Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter'] | train.py models.py gat.py layers.py utils.py gcn.py SpGAT GCN GraphConvolution SpGraphAttentionLayer SpecialSpmm SpecialSpmmFunction Model evaluate pass_data_iteratively test adjust_learning_rate train evaluation_4class normalize_adj sparse_mx_to_torch_sparse_tensor load_user_tweet_graph accuracy build_symmetric_adjace... | # HGATRD The implementation of our IJCNN 2020 paper "Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter" # Requirements python 3.6.6 numpy==1.17.2 scipy==1.3.1 pytorch==1.1.0 scikit-learn==0.21.3 # How to use ## Dataset | 11 |
21lva/EEN_Tensorflow | ['video prediction'] | ['Prediction Under Uncertainty with Error-Encoding Networks'] | visualize.py new_train_d.py new_models.py new_train_s.py utils.py dataloaders/data_atari.py LatentNetwork DeterministicNetwork validation_epoch train train_epoch validation_epoch train train_epoch read_data log load_model ImageLoader get_batch print epoch_size apply_gradients compute_gradients range compute_gradients e... | # EEN_Tensorflow error encoding network by tensorflow only for breakout. The paper of EEN : https://arxiv.org/abs/1711.04994 | 12 |
21lva/EEN_acrobot | ['video prediction'] | ['Prediction Under Uncertainty with Error-Encoding Networks'] | train_d.py expert_policies/utils/tools.py pa.py models.py predict.py utilsf.py train_s.py changeJson.py LatentNetwork DeterministicNetwork DataMaker load_model DrawGraph invScaling BaseLineModelPredictor mape get_cond LatentModelPredictor validation_epoch train train_epoch validation_epoch train train_epoch read_data l... | ### EEN_acrobot Error encoding network for acrobot error encoding network by tensorflow.keras. The paper of EEN : https://arxiv.org/abs/1711.04994 need: python>=3.5 tensorflow (any version that support tf.keras) numpy matplotlib etc.... | 13 |
3778/Ward2ICU | ['time series'] | ['Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs', 'Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward'] | tests/test_samplers.py run-experiment.py tests/test_models.py ward2icu/models/classifiers.py ward2icu/metrics.py ward2icu/models/cnngan.py ward2icu/models/__init__.py ward2icu/trainers.py ward2icu/samplers.py tests/test_data.py ward2icu/models/rgan.py ward2icu/utils.py ward2icu/models/rcgan.py tests/test_utils.py tests... | --- <div align="center"> # Ward2ICU [](https://arxiv.org/abs/1910.00752) [](https://research.3778.care/projects/privacy/) [.*
---
This repository contains the source code related to the paper [*"On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks"*](http://arxiv.org/abs/2003.0874... | 15 |
4kubo/bacf_python | ['visual tracking'] | ['Learning Background-Aware Correlation Filters for Visual Tracking'] | background_aware_correlation_filter.py special_operation/convertor.py utils/arg_parse.py special_operation/resp_newton.py utils/report.py utils/get_sequence.py image_process/feature.py utils/functions.py demo_on_otb.py BackgroundAwareCorrelationFilter show_image_with_bbox get_pixel get_pyhog get_pixels resize_DFT2 resp... | # A port of [BACF in Matlab](http://www.hamedkiani.com/bacf.html) to python 2 Python 2 implementation of Background-Aware Correlation Filters for visual tracking. For more detail, please refer to [arXiv paper](https://arxiv.org/abs/1703.04590) and [the author's website ](http://www.hamedkiani.com/bacf.html) ![tracking_... | 16 |
50kawa/mimick_chainer | ['word embeddings'] | ['Mimicking Word Embeddings using Subword RNNs'] | make_wordlist.py data_make.py util.py eval_wordsimilarity.py main.py model.py ngram zijougosa cos_sim cos_sim load_wordvector mse main calc_batch_loss cos_sim EncoderCNN load_model Interpreter Encoder EncoderGRU EncoderSumFF Model EncoderSum SplitWord Dataread PWIMdata load_word2vec_format load_fasttext_format random_c... | # mimick_chainer https://arxiv.org/abs/1707.06961 の実装をchainerでやってみました。 ## 動かし方 | 17 |
590shun/vsum_dsf | ['video summarization'] | ['Video Summarization using Deep Semantic Features'] | script/summarize.py func/sampling/vsum.py func/dataset/summe.py script/evaluate.py script/uniform_smpl.py func/nets/vid_enc.py func/nets/vid_enc_vgg19.py SUMME Model Model representativeness encodeSeg uniformity VSUM eval_human_summary eval_summary eval_f1 get_flabel uniform_sampling model reduce from_numpy feat sample... | # Video Summarization using Deep Semantic Features これは"Video Summarization using Deep Semantic Features" in ACCV'16 [[arXiv](arxiv.org/abs/1609.08758)]を書き直したものになります。 [実装元のリンク](http://github.com/mayu-ot/vsum_dsf),[備忘録](https://github.com/590shun/paper_challenge/issues/7) ## 実験手順 git clone https://github.com/590shun/v... | 18 |
5gon12eder/msc-graphstudy | ['graph generation', 'data augmentation'] | ['Aesthetic Discrimination of Graph Layouts'] | driver/quarry.py benchmarks/lib/history.py driver/www/property.py driver/www/graph.py utils/prepare-local-diffent-regression.py benchmarks/lib/__init__.py eval/progress.py driver/__init__.py eval/make-config-puncture.py utils/prepare-entropy-regression.py benchmarks/micro-driver.py driver/model.py driver/www/nn.py driv... | <!-- -*- coding:utf-8; mode:markdown; -*- --> <!-- Copyright (C) 2018 Moritz Klammler <moritz.klammler@alumni.kit.edu> --> <!-- --> <!-- Permission is granted to co... | 19 |
7-B/yoco | ['style transfer'] | ['Deep Photo Style Transfer'] | server.py app.py wsgi.py segmentation/torch_neural_style_transfer.py segmentation/merge_image.py convert.py segmentation/evaluate.py main add_header coloring github sketch image_resize convert_to_line png2svg simplify sobel main get_palette get_arguments image_resize image_loader StyleLoss Normalization gram_matrix ims... | <h1 align="center"> <br> <a href="https://141.223.140.22"><img src="img/YOCO-logo.png" alt="YOCO" width="200"><a> <br> </h1> ## Contents - [**Weekly Record**](https://github.com/7-B/yoco/wiki/Development-Record) - [**Reference**](https://github.com/7-B/yoco/wiki/%EC%B0%B8%EA%B3%A0-%EC%9E%90%EB%A3%8C) - [**D... | 20 |
9ruddls3/CRNN_Pytorch | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | prepare.py train.py parms.py model/CRNN.py Preparing reset model_train BiDireRNN ToRNN CNN_block CRNN_model str list sort convert Compose tqdm abspath device append to listdir enumerate len hasattr isinstance xavier_normal fill_ Conv2d modules reset_parameters zero_ BatchNorm2d weight Linear train_model join backward p... | # CRNN_Pytorch 이 CRNN 패키지는 제 데이터셋을 기반으로 진행하였고, 구글 코랩을 기반으로 진행하였습니다. 전반적인 Hyper-Parameter는 prams.py 에서 선언되었으며, 이를 수정함으로써 튜닝 할 수 있습니다. 문자열 이미지데이터가 있고, 파일명이 'label.확장자'로 되어있다면, prepare.py 를 통해 Dataloader 로 변환 할 수 있습니다. This package is for my custom Dataset and based on Google Colaborative. If you use this for ... | 21 |
A-Jacobson/Depth_in_The_Wild | ['depth estimation'] | ['Single-Image Depth Perception in the Wild'] | train.py datasets.py models.py train_utils.py criterion.py RelativeDepthLoss NYUDepth HourGlass ConvReluBN InceptionS InceptionL main validate AverageMeter save_checkpoint _fit_epoch prep_img fit NYUDepth load show state_dict plot xlabel ylabel RMSprop parameters save_checkpoint load_state_dict RelativeDepthLoss legend... | # Depth in The Wild Pytorch implementation of Single-Image Depth Perception in the Wild https://arxiv.org/pdf/1604.03901.pdf - [x] Data Loader for NYUDepth - [x] Architecture - [x] Custom Criterion - [x] Train on Small sample - [x] Tweak Architecture - [ ] Fully Train Model (310/~500 epochs) - [ ] Validate Results ## N... | 22 |
A-ZHANG1/PSENet | ['optical character recognition', 'scene text detection', 'curved text detection'] | ['Shape Robust Text Detection with Progressive Scale Expansion Network', 'Shape Robust Text Detection with Progressive Scale Expansion Network'] | util/statistic.py util/tf.py pypse.py util/event.py train_ic15.py models/pvanet.py metrics.py util/feature.py util/proc.py test_ctw1500.py util/rand.py util/t.py util/neighbour.py util/caffe_.py models/__init__.py util/dtype.py dataset/icdar2015_loader.py util/ml.py util/str_.py utils.py util/test.py util/log.py test_i... | # Shape Robust Text Detection with Progressive Scale Expansion Network ## Requirements * Python 2.7 * PyTorch v0.4.1+ * pyclipper * Polygon2 * OpenCV 3.4 (for c++ version pse) * opencv-python 3.4 ## Introduction Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-s... | 23 |
AADeLucia/gpt2-narrative-decoding | ['response generation'] | ['Decoding Methods for Neural Narrative Generation'] | data/count_length.py mturk_human_evaluation/fleiss.py mturk_human_evaluation/cohen.py evaluation.py generate_responses.py mturk_human_evaluation/format_generated_narratives_csv.py data/preproc.py mturk_human_evaluation/create_html_survey.py sentBERT distinct_2 distinct_1 clean_response parse_args parse_args batchify pr... | # Decoding Methods for Neural Narrative Generation Alexandra DeLucia\*, Aaron Mueller\*, Xiang "Lisa" Li, João Sedoc This repository contains code for replicating the approach and results of the paper [Decoding Strategies for Neural Narrative Generation](https://arxiv.org/abs/2010.07375). ## Models Our GPT-2 Medium mod... | 24 |
AI-HPC-Research-Team/AIPerf | ['automl'] | ['AIPerf: Automated machine learning as an AI-HPC benchmark'] | examples/trials/network_morphism/cifar10/distributed_utils.py src/sdk/pynni/tests/models/pytorch_models/__init__.py tools/nni_cmd/command_utils.py tools/nni_cmd/url_utils.py tools/nni_annotation/testcase/usercode/nas.py src/nni_manager/training_service/test/mockedTrial.py src/sdk/pynni/tests/test_compressor.py src/sdk/... |    **<font size=4>开发单位：鹏城实验室(PCL)，清华大学(THU)</font>** **<font size=4>特别感谢国防科技大学窦勇老师及其团队的宝贵意见和支持</... | 25 |
AI-secure/VeriGauge | ['autonomous driving'] | ['SoK: Certified Robustness for Deep Neural Networks'] | convex_adversarial/examples/cifar.py recurjac/task_lipschitz.py convex_adversarial/examples/fashion_mnist.py cnn_cert/setup_mnist.py cnn_cert/train_resnet.py cnn_cert/CLEVER/setup_mnist.py recurjac/bound_fastlin_fastlip.py basic/core.py cnn_cert/CLEVER/nlayer_model.py cnn_cert/CLEVER/randsphere.py models/cnn_cert_model... | AI-secure/VeriGauge | 26 |
AIEMMU/MRI_Prostate | ['medical image segmentation', 'medical diagnosis'] | ['Deep learning in magnetic resonance prostate segmentation: A review and a new perspective'] | app/contour.py app/predictor.py app/viewer.py app/hooks/hook-fastprogress.py app/hooks/hook-PySide2.QtWebEngineWidgets.py app/hooks/hook-torchvision.py app/ui.py app/displayView.py app/utils.py app/displayViewModel.py app/dicom.py Contour dicom MainWindow DisplayViewModel predictor get_y enableButtons listify loadConto... | # Automatic Prostate segmentation from MR images This provides some deep Learning code for training a deep learning model for segmentation from Magnetic Resonance(MR) Images. There is also a pyqt software avaialble to allow users to explore the use of the deep learning model that was trained as part of this project. Mo... | 27 |
AIS-Bonn/FreqNet | ['video prediction'] | ['Frequency Domain Transformer Networks for Video Prediction'] | BBall/data_dynamic.py MMNIST/data_dynamic.py show_single_V bounce_n ar bounce_vec bounce_mat sigmoid new_speeds unsigmoid show_A matricize show_V show_single_V bounce_n ar bounce_vec bounce_mat sigmoid new_speeds unsigmoid show_A matricize show_V int norm randn transpose rand dot new_speeds zeros abs array range ar mes... | # Frequency Domain Transformer Networks for Video Prediction If you use the code for your research paper, please cite the following paper: <p> Hafez Farazi<b></b>, and Sven Behnke:<br> <a href="https://arxiv.org/pdf/1903.00271.pdf"><u>Frequency Domain Transformer Networks for Video Prediction</u></a> <a href="... | 28 |
AIS-Bonn/LocDepVideoPrediction | ['video prediction'] | ['Location Dependency in Video Prediction'] | data_dynamic.py show_single_V bounce_n ar bounce_vec bounce_mat sigmoid new_speeds unsigmoid show_A matricize show_V int norm randn transpose rand dot new_speeds zeros abs array range ar meshgrid zeros array range matricize bounce_n array matricize bounce_n array show int sqrt reshape show int print reshape sqrt range ... | # LocDepVideoPrediction If you use the code for your research paper, please cite the following paper: <p> Niloofar Azizi<b>*</b>, Hafez Farazi<b>*</b>, and Sven Behnke:<br> <a href="http://www.ais.uni-bonn.de/~hfarazi/papers/LocDep.pdf"><u>Location Dependency in Video Prediction</u></a> <a href="http://www.... | 29 |
ALBERT-Inc/blog_ssap | ['instance segmentation', 'semantic segmentation'] | ['SSAP: Single-Shot Instance Segmentation With Affinity Pyramid'] | src/loss.py src/graph_partition.py src/SSAP.py src/mydatasets.py greedy_additive calc_js_div Edge make_ins_seg Partition calc_loss l2_loss focal_loss preprocess Mydatasets Up DoubleConv Down OutConv SSAP list heapify contract heappop removed heappush exp clip sum log greedy_additive sorted calc_js_div list ones nodes w... | # SSAP再現実装公開用リポジトリ [SSAP [Proposal-freeなInstance Segmentation手法] の紹介と実験](https://blog.albert2005.co.jp/2020/08/18/ssap/)で使用したプログラムです. ## ファイル構成について ``` ├─ README.md # プロジェクトの説明 ├─ requirements.txt # すべてのPythonプログラムのベースとなるPythonパッケージ ├─ data/ # データフォルダ... | 30 |
AMLab-Amsterdam/CEVAE | ['causal inference'] | ['Causal Effect Inference with Deep Latent-Variable Models'] | cevae_ihdp.py utils.py datasets.py evaluation.py IHDP Evaluator get_y0_y1 fc_net format print write mean range flush | # CEVAE This repository contains the code for the Causal Effect Variational Autoencoder (CEVAE) model as developed at [1]. This code is provided as is and will not be updated / maintained. Sample experiment --- To perform a sample run of CEVAE on 10 replications of the Infant Health and Development Program (IHDP) da... | 31 |
AMLab-Amsterdam/SEVDL_MGP | ['gaussian processes'] | ['Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors'] | mnist_classification.py optimizer.py nn_utils.py layers.py yacht_regression.py matrix_layers.py VMGNet.py sample_mgaus sample_gauss sample_mult_noise add_bias kldiv_gamma sample_mgaus2 Layer MatrixGaussDiagLayerLearnP MatrixGaussDiagLayerFF randmat randmat2 randvector tscalar multvector log_f Polygamma change_random_se... | Example implementation of the Bayesian neural network in: ***"Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors"***, Christos Louizos & Max Welling, ICML 2016, ([https://arxiv.org/abs/1603.04733]()) This code is provided as is and will not be maintained / updated. | 32 |
ANLGBOY/SoftFlow | ['point cloud generation'] | ['SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds'] | toy_example/lib/layers/diffeq_layers/__init__.py toy_example/generate2.py toy_example/lib/layers/diffeq_layers/basic.py pointclouds/metrics/pytorch_structural_losses/nn_distance.py toy_example/lib/layers/container.py toy_example/lib/layers/odefunc.py pointclouds/utils.py pointclouds/metrics/pytorch_structural_losses/se... | # SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds This repository provides the implementation of SoftFlow on toy dataset and point clouds. Move to each folder, follow the instructions and enjoy the results! ## Overview <p align="center"> <img src="assets/training_technique.png" height=256/> </p>... | 33 |
ANLGBOY/WaveNODE | ['speech synthesis'] | ['WaveNODE: A Continuous Normalizing Flow for Speech Synthesis'] | preprocessing.py train.py data.py odefunc.py hps.py args.py layers.py synthesize.py test_speed.py test_cll.py utils.py model.py parse_args _pad collate_fn_synthesize _pad_2d collate_fn LJspeechDataset Hyperparameters fused_add_tanh_sigmoid_multiply ActNorm CNF WaveNetPrior logabs MovingBatchNorm1d MBNLayer remove NODEB... | # Pytorch Implementation of WaveNODE PyTorch implementation of WaveNODE ## Abstract In recent years, various flow-based generative models have been proposed to generate highfidelity waveforms in real-time. However, these models require either a well-trained teacher network or a number of flow steps making them memory-i... | 34 |
APooladian/ProxLogBarrierAttack | ['adversarial attack'] | ['A principled approach for generating adversarial images under non-smooth dissimilarity metrics'] | mnist-example/model/utils.py proxlogbarrier_Top1.py mnist-example/model/__init__.py proxlogbarrier_Top5.py mnist-example/model/mnist.py InitMethods.py prox.py mnist-example/run_attack.py mnist-example/model/blocks.py simplex.py GaussianInitialize UniformInitialize BlurInitialize HeatSmoothing SafetyInitialize L2NormPro... | # ProxLogBarrierAttack Public repository for the ProxLogBarrier attack, described in [A principled approach for generating adversarial images under non-smooth dissimilarity metrics](https://arxiv.org/abs/1908.01667). Abstract: Deep neural networks perform well on real world data, but are prone to adversarial perturbati... | 35 |
ARMargolis/melanoma-pytorch | ['network pruning'] | ['The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks'] | model/data.py scripts/train_model.py model/model.py MelanomaDataset MelanomaNet BasicBlock BottleneckBlock | Goal: A PyTorch model for https://www.kaggle.com/c/siim-isic-melanoma-classification/data Technique: Pruning https://pytorch.org/tutorials/intermediate/pruning_tutorial.html#iterative-pruning Theory: Lottery Ticket Hypothesis https://arxiv.org/pdf/1803.03635.pdf # Setup ## Colab stuff To use the kaggle API in CoLab, fo... | 36 |
ASMIftekhar/VSGNet | ['human object interaction detection'] | ['VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions'] | scripts_hico/HICO_eval/sample_complexity_analysis.py scripts/calculate_ap_classwise.py scripts_hico/train_test.py scripts/dataloader_vcoco.py scripts/prior_vcoco.py scripts_hico/helpers_preprocess.py scripts_hico/calculate_map_vcoco.py scripts_hico/model.py scripts_hico/HICO_eval/bbox_utils.py scripts/pred_vis.py scrip... | # VSGNet ### [**VSGNet:Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions**](http://openaccess.thecvf.com/content_CVPR_2020/papers/Ulutan_VSGNet_Spatial_Attention_Network_for_Detecting_Human_Object_Interactions_Using_CVPR_2020_paper.pdf) [Oytun Ulutan*](https://sites.google.com... | 37 |
AWLyrics/SeqGAN_Poem | ['text generation'] | ['SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient'] | lyric_preprocessing.py preprocessing.py bleu_calc.py dataloader.py mydis.py myG_beta.py rhyme.py seq_gan.py data/preprocess.py mygen.py id2poem.py session_save.py translate.py calc_bleu Input_Data_loader Gen_Data_loader Dis_dataloader load_data linear highway Discriminator Generator G_beta rhyme2 rhyme1 generate_sample... | # SeqGAN Poem 修改 SeqGAN 用于诗歌生成（去除 Oracle LSTM） ## Pipeline - 收集唐诗预处理（preprocessing.py），唐诗[数据集](https://github.com/chinese-poetry/chinese-poetry)，选取了 5000 首五言律诗（20 词） - Tokenize，将中文字转换为 index（train.txt），建立词表（dict.pkl），记录 vocab size - Pretrain Generation - Adersarial Training ## Reference Paper: https://arxiv.org/abs/160... | 38 |
End of preview. Expand in Data Studio
No dataset card yet
- Downloads last month
- 5