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代码论文怎么写最近几篇较好论文实现代码（附源代码下载）

作者：ZQ我是疯癫小karmenRJ | 来源：互联网 | 2023-08-19 10:10

本文主要分享【代码论文怎么写】，技术文章【最近几篇较好论文实现代码（附源代码下载）】为【计算机视觉研究院】投稿，如果你遇到相关问题，本文相关知识或能到你。代码论文怎么写关注并星标从此不迷路

本文主要分享【代码论文怎么写】，技术文章【最近几篇较好论文实现代码（附源代码下载）】为【计算机视觉研究院】投稿，如果你遇到相关问题，本文相关知识或能到你。

代码论文怎么写

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《Towards Layer-wise Image Vectorization》(CVPR 2022)

GitHub: github.com/ma-xu/LIVE

Installation

We suggest users to use the conda for creating new python environment.

Requirement: 5.0 10.0.

git clone git@github.com:ma-xu/LIVE.gitcd LIVE
conda create -n live python=3.7
conda activate live
conda install -y pytorch torchvision -c pytorch
conda install -y numpy scikit-image
conda install -y -c anaconda cmake
conda install -y -c conda-forge ffmpeg
pip install svgwrite svgpathtools cssutils numba torch-tools scikit-fmm easydict visdom
pip install opencv-python==4.5.4.60  # please install this version to avoid segmentation fault.cd DiffVG
git submodule update --init --recursive
python setup.py installcd ..

Run Experiments

conda activate live
cd LIVE
# Please modify the paramters accordingly.
python main.py --config 
  
    --experiment 
   
     --signature 
    
      --target 
     
       --log_dir 
      
        # Here is an simple example: python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/

《Multimodal Token Fusion for Vision Transformers》(CVPR 2022)

GitHub: github.com/yikaiw/TokenFusion

《PointAugmenting: Cross-Modal Augmentation for 3D Object Detection》(CVPR 2022)

GitHub: github.com/VISION-SJTU/PointAugmenting

《Fantastic questions and where to find them: FairytaleQA -- An authentic dataset for narrative comprehension.》(ACL 2022)

GitHub: github.com/uci-soe/FairytaleQAData

《LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks》(AAAI 2022)

GitHub: github.com/agoodge/LUNAR

Firstly, extract data.zip

To replicate the results on the HRSS dataset with neighbour count k = 100 and "Mixed" negative sampling scheme

Extract saved_models.zip

Run:

python3 main.py --dataset HRSS --samples MIXED --k 100

To train a new model:

python3 main.py --dataset HRSS --samples MIXED --k 100 --train_new_model

《Pseudo-Label Transfer from Frame-Level to Note-Level in a Teacher-Student Framework for Singing Transcription from Polyphonic Music》(ICASSP 2022)

GitHub: github.com/keums/icassp2022-vocal-transcription

《Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion》(ICASSP 2022)

GitHub: github.com/jlian2/Robust-Voice-Style-Transfer

Demo：https://jlian2.github.io/Robust-Voice-Style-Transfer/

《HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers》(ICRA 2022)

GitHub: github.com/NVlabs/handover-sim

2022-06-03 16:13:46: Running evaluation for results/2022-02-28_08-57-34_yang-icra2021_s0_test
2022-06-03 16:13:47: Evaluation results:
|  success rate   |    mean accum time (s)    |                    failure (%)                     |
|      (%)        |  exec  |  plan  |  total  |  hand contact   |   object drop   |    timeout     |
|:---------------:|:------:|:------:|:-------:|:---------------:|:---------------:|:--------------:|
| 64.58 ( 93/144) | 4.864  | 0.036  |  4.900  | 17.36 ( 25/144) | 11.81 ( 17/144) | 6.25 (  9/144) |
2022-06-03 16:13:47: Printing scene ids
2022-06-03 16:13:47: Success (93 scenes):
---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---
  0    1    2    3    4    5    6    7    8    9   10   12   13   15   16   17   18   19   21   22
 23   25   26   27   28   30   33   34   35   36   37   38   42   43   46   49   50   53   54   56
 59   60   62   63   64   66   68   69   70   71   72   77   81   83   85   87   89   91   92   93
 94   95   96   98  103  106  107  108  109  110  111  112  113  114  115  116  117  120  121  123
125  126  127  128  130  131  132  133  137  138  139  141  143
---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---
2022-06-03 16:13:47: Failure - hand contact (25 scenes):
---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---
 11   14   20   29   39   40   41   44   45   47   51   55   57   58   65   67   74   80   82   88
102  105  118  124  136
---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---
2022-06-03 16:13:47: Failure - object drop (17 scenes):
---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---
 24   31   32   52   61   78   79   84   86   97  101  104  119  122  134  140  142
---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---
2022-06-03 16:13:47: Failure - timeout (9 scenes):
---  ---  ---  ---  ---  ---  ---  ---  ---
 48   73   75   76   90   99  100  129  135
---  ---  ---  ---  ---  ---  ---  ---  ---
2022-06-03 16:13:47: Evaluation complete.

《CDLM: Cross-Document Language Modeling》(EMNLP 2021)

GitHub: github.com/aviclu/CDLM

You can either pretrain by yourself or use the pretrained CDLM model weights and tokenizer files, which are available on HuggingFace.

Then, use：

from transformers import AutoTokenizer, AutoModel
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('biu-nlp/cdlm')
model = AutoModel.from_pretrained('biu-nlp/cdlm')

《Continual Learning for Task-Oriented Dialogue Systems》(EMNLP 2021)

GitHub: github.com/andreamad8/ToDCL

《Torsional Diffusion for Molecular Conformer Generation》(2022)

GitHub: github.com/gcorso/torsional-diffusion

《MMChat: Multi-Modal Chat Dataset on Social Media》(2022)

GitHub: github.com/silverriver/MMChat

《Can CNNs Be More Robust Than Transformers?》(2022)

GitHub: github.com/UCSC-VLAA/RobustCNN

《Revealing Single Frame Bias for Video-and-Language Learning》(2022)

GitHub: github.com/jayleicn/singularity

《Progressive Distillation for Fast Sampling of Diffusion Models》(2022)

GitHub: github.com/Hramchenko/diffusion_distiller

《Neural Basis Models for Interpretability》(2022)

GitHub: github.com/facebookresearch/nbm-spam

《Scalable Interpretability via Polynomials》(2022)

GitHub: github.com/facebookresearch/nbm-spam

《Infinite Recommendation Networks: A Data-Centric Approach》(2022)

GitHub: github.com/noveens/infinite_ae_cf

《The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling》(2022)

GitHub: github.com/radi-cho/GatedTabTransformer

Usage：

import torch
import torch.nn as nn
from gated_tab_transformer import GatedTabTransformer


model = GatedTabTransformer(
    categories = (10, 5, 6, 5, 8),      # tuple containing the number of unique values within each category
    num_cOntinuous= 10,                # number of continuous values
    transformer_dim = 32,               # dimension, paper set at 32
    dim_out = 1,                        # binary prediction, but could be anything
    transformer_depth = 6,              # depth, paper recommended 6
    transformer_heads = 8,              # heads, paper recommends 8
    attn_dropout = 0.1,                 # post-attention dropout
    ff_dropout = 0.1,                   # feed forward dropout
    mlp_act = nn.LeakyReLU(0),          # activation for final mlp, defaults to relu, but could be anything else (selu, etc.)
    mlp_depth=4,                        # mlp hidden layers depth
    mlp_dimension=32,                   # dimension of mlp layers
    gmlp_enabled=True                   # gmlp or standard mlp
)


x_categ = torch.randint(0, 5, (1, 5))   # category values, from 0 - max number of categories, in the order as passed into the constructor above
x_cOnt= torch.randn(1, 10)             # assume continuous values are already normalized individually


pred = model(x_categ, x_cont)
print(pred)

《Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition》(2022)

GitHub: github.com/yaoing/DAN

《Towards Principled Disentanglement for Domain Generalization》(2021)

GitHub: github.com/hlzhang109/DDG

《SoundStream: An End-to-End Neural Audio Codec》(2021)

GitHub: github.com/wesbz/SoundStream

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