【MindSpore开发者群英会】经典论文复现活动

### 任务描述 
参考任务清单中paperswithcode链接，学习论文中的网络模型，基于MindSpore框架完成对应网络模型的复现。

### 任务要求

1. 建议基于MindSpore框架2.0.0版本进行开发，不能使用其他AI框架的接口。最新版本安装请参考：https://www.mindspore.cn/install  选择2.0.0版本。

2. 任务分为两部分：

①【推理】完成网络模型和权重转成MindSpore格式并做推理；

②【训练】在完成推理之后，需要完成从0到1的训练；

3. 代码提交至个人github开源仓库，并在paperswithcode网站对应的论文下添加自己的代码实现。

4. 代码完成之后，在[昇思MindSpore论坛](https://www.hiascend.com/forum/forum-0106101385921175002-1.html)分享自己的项目实现过程与经验。

### 注意事项

1. 请在本issue评论区输入“认领+序号”来领取该任务，例如评论“认领6”即为认领序号为6的任务。如果是组队认领则一人评论即可。
2. 完成任务后需要在本issues下面评论“任务+序号+已完成（推理/训推）”来结束对应的任务，例如评论“任务6已完成推理”，“任务9已完成训推”。
3. 该任务可单人可组队完成，组队人数不要超过5人。

### 奖品领取规则

1.代码完成之后，在昇思MindSpore论坛平台发帖分享自己的项目实现过程与经验。
2.将发帖链接、代码链接以及邮寄地址和联系方式发给【活动助手】，经审核老师验证通过后即可发放。

| 奖品                             | 要求                 | 
| :------------------------------- | -------------------- |
| 华为AI音响2星云白                | 完成1个推理任务      |
| 华为HUAWEI FreeLace Pro 无线耳机 | 完成1个推理任务      | 
| HUAWEI WATCH GT 2e               | 完成2个推理任务      | 
| 华为手环 B5 时尚版 钛金灰        | 完成2个推理任务      | 
| 荣耀50SE                         | 完成1个推理&训练任务 |

| 序号 | paperswithcode链接                                           | 认领人 | 状态 |
| :--: | ------------------------------------------------------------ | :-----: | :--: |
|  1   | <https://paperswithcode.com/paper/190500641>                 |第一可爱啊啊啊啊|奖品已发放|
|  2   | <https://paperswithcode.com/paper/190807919>                 | Caly |奖品已发放|
|  3   | https://paperswithcode.com/paper/accurate-large-minibatch-sgd-training |        | 已认领 |
|  4   | https://paperswithcode.com/paper/adam-a-method-for-stochastic-optimization |wangyixuan1|奖品已发放|
|  5   | <https://paperswithcode.com/paper/addressing-function-approximation-error-in> |       |已认领|
|  6   | https://paperswithcode.com/paper/albert-a-lite-bert-for-self-supervised |        |已认领 |
|  7   | https://paperswithcode.com/paper/analyzing-and-improving-the-image-quality-of |zhourulin|审核中|
|  8   | https://paperswithcode.com/paper/an-end-to-end-trainable-neural-network-for |        |已认领 |
|  9   | <https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style> | wyyang | 已认领 |
|  10  | <https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1> | 周大山 |奖品已发放 |
|  11  | https://paperswithcode.com/paper/arcface-additive-angular-margin-loss-for-deep | wwwwxl | 已认领 |
|  12  | <https://paperswithcode.com/paper/a-structured-self-attentive-sentence> |mekbibumebratu|已认领|
|  13  | <https://paperswithcode.com/paper/a-style-based-generator-architecture-for> |        |已认领|
|  14  | https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement |        |已认领 |
|  15  | <https://paperswithcode.com/paper/attention-is-all-you-need> |  vchopin |   已认领   |
|  16  | https://paperswithcode.com/paper/attention-u-net-learning-where-to-look-for | zhourulin | 奖品已发放 |
|  17  | https://paperswithcode.com/paper/auto-encoding-variational-bayes |wangyixuan1 | 奖品已发放 |
|  18  | <https://paperswithcode.com/paper/beyond-part-models-person-retrieval-with> | Caly|奖品已发放|
|  19  | https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to |keep a distance|已认领 |
|  20  | https://paperswithcode.com/paper/bottom-up-and-top-down-attention-for-image | lulin | 已认领 |
|  21  | https://paperswithcode.com/paper/can-spatiotemporal-3d-cnns-retrace-the |        |      |
|  22  | https://paperswithcode.com/paper/conditional-generative-adversarial-nets | Woretaw | 奖品已发放 |
|  23  | https://paperswithcode.com/paper/continuous-control-with-deep-reinforcement | yxyttt| 已认领 |
|  24  | <https://paperswithcode.com/paper/convolutional-neural-networks-for-sentence> | Negus| 已完成奖品发放 |
|  25  | <https://paperswithcode.com/paper/convolutional-pose-machines> |        | 已认领 |
|  26  | https://paperswithcode.com/paper/cutmix-regularization-strategy-to-train |  weiwan | 已认领 |
|  27  | https://paperswithcode.com/paper/cyclical-learning-rates-for-training-neural |        | 已认领 |
|  28  | https://paperswithcode.com/paper/darts-differentiable-architecture-search |        |已认领 |
|  29  | https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q | Caly|已认领|
|  30  | <https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition> | faumoc | 已认领     |
|  31  | <https://paperswithcode.com/paper/densely-connected-convolutional-networks> | YechaoZhang| 已认领 |
|  32  | <https://paperswithcode.com/paper/diverse-beam-search-decoding-diverse> |        |已认领|
|  33  | https://paperswithcode.com/paper/domain-adversarial-training-of-neural |        |已认领|
|  34  | https://paperswithcode.com/paper/dueling-network-architectures-for-deep |        |已认领|
|  35  | https://paperswithcode.com/paper/dynamic-routing-between-capsules |        | 已认领 |
|  36  | https://paperswithcode.com/paper/effective-approaches-to-attention-based | ABUSH SAHLU |已认领 |
|  37  | https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object |        |已认领|
|  38  | https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for | XL | 已认领 |
|  39  | https://paperswithcode.com/paper/enhanced-deep-residual-networks-for-single |        |已认领|
|  40  | https://paperswithcode.com/paper/explaining-and-harnessing-adversarial | YechaoZhang | 已认领 |
|  41  | https://paperswithcode.com/paper/exploring-simple-siamese-representation |        |已认领|
|  42  | https://paperswithcode.com/paper/facenet-a-unified-embedding-for-face |        |已认领|
|  43  | https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object |Negus|已认领 |
|  44  | https://paperswithcode.com/paper/fastspeech-2-fast-and-high-quality-end-to-end |       |已认领|
|  45  | https://paperswithcode.com/paper/fcos-fully-convolutional-one-stage-object |Lifang_xiao |已领取|
|  46  | https://paperswithcode.com/paper/feature-pyramid-networks-for-object-detection | unseenme | 已领取 |
|  47  | https://paperswithcode.com/paper/focal-loss-for-dense-object-detection | Im98tyx | 已领取 |
|  48  | https://paperswithcode.com/paper/gans-trained-by-a-two-time-scale-update-rule |        |已认领|
|  49  | https://paperswithcode.com/paper/generative-adversarial-networks | RiskyZh | 已领取   |
|  50  | https://paperswithcode.com/paper/grad-cam-visual-explanations-from-deep |        |已认领 |
|  51  | https://paperswithcode.com/paper/graph-attention-networks    |   Negus   |   已完成奖品发放   |
|  52  | https://paperswithcode.com/paper/hand-keypoint-detection-in-single-images |        |已认领 |
|  53  | https://paperswithcode.com/paper/image-inpainting-for-irregular-holes-using |        |已认领|
|  54  | https://paperswithcode.com/paper/image-to-image-translation-with-conditional |  TianyiLi | 已领取 |
|  55  | https://paperswithcode.com/paper/improved-baselines-with-momentum-contrastive | 明天天气不错 |已领取 |
|  56  | https://paperswithcode.com/paper/improved-techniques-for-training-gans | lee  | 已领取 |
|  57  | https://paperswithcode.com/paper/improved-training-of-wasserstein-gans | Negus |已领取|
|  58  | https://paperswithcode.com/paper/learning-transferable-visual-models-from |        |已认领 |
|  59  | https://paperswithcode.com/paper/listen-attend-and-spell     |        |已认领 |
|  60  | https://paperswithcode.com/paper/masked-autoencoders-are-scalable-vision |        |已认领 |
|  61  | <https://paperswithcode.com/paper/mask-r-cnn>                | Kurunie | 已领取 |
|  62  | https://paperswithcode.com/paper/mixmatch-a-holistic-approach-to-semi |        |已认领|
|  63  | https://paperswithcode.com/paper/mixup-beyond-empirical-risk-minimization | YechaoZhang |已领取|
|  64  | https://paperswithcode.com/paper/mobilenets-efficient-convolutional-neural | 陆建荣 | 已领取 |
|  65  | https://paperswithcode.com/paper/mobilenetv2-inverted-residuals-and-linear | 陆建荣 | 已领取 |
|  66  | https://paperswithcode.com/paper/model-agnostic-meta-learning-for-fast |        |已认领|
|  67  | https://paperswithcode.com/paper/momentum-contrast-for-unsupervised-visual |        |已认领|
|  68  | https://paperswithcode.com/paper/multi-agent-actor-critic-for-mixed |        |已认领|
|  69  | https://paperswithcode.com/paper/natural-tts-synthesis-by-conditioning-wavenet |       |已认领|
|  70  | https://paperswithcode.com/paper/neural-discrete-representation-learning |        |已认领|
|  71  | https://paperswithcode.com/paper/neural-machine-translation-by-jointly |        |已认领 |
|  72  | https://paperswithcode.com/paper/neural-ordinary-differential-equations |        |已认领|
|  73  | https://paperswithcode.com/paper/objects-as-points           |        |已认领 |
|  74  | https://paperswithcode.com/paper/perceptual-losses-for-real-time-style |        |已认领|
|  75  | https://paperswithcode.com/paper/performance-measures-and-a-data-set-for-multi |       |已认领|
|  76  | https://paperswithcode.com/paper/person-transfer-gan-to-bridge-domain-gap-for |        |已认领|
|  77  | https://paperswithcode.com/paper/photo-realistic-single-image-super-resolution |Woretaw|已认领|
|  78  | https://paperswithcode.com/paper/playing-atari-with-deep-reinforcement |Wonham|审核中|
|  79  | <https://paperswithcode.com/paper/pointnet-deep-hierarchical-feature-learning> |       |已认领|
|  80  | https://paperswithcode.com/paper/pointnet-deep-learning-on-point-sets-for-3d |        |已认领|
|  81  | https://paperswithcode.com/paper/prioritized-experience-replay |        |已认领 |
|  82  | https://paperswithcode.com/paper/progressive-growing-of-gans-for-improved |        |已认领 |
|  83  | https://paperswithcode.com/paper/prototypical-networks-for-few-shot-learning |JwYu|已领取|
|  84  | https://paperswithcode.com/paper/proximal-policy-optimization-algorithms |        |已认领|
|  85  | https://paperswithcode.com/paper/pyramid-scene-parsing-network | HenonBamboo | 已完成奖品发放 |
|  86  | https://paperswithcode.com/paper/rainbow-combining-improvements-in-deep |        |已认领|
|  87  | <https://paperswithcode.com/paper/realtime-multi-person-2d-pose-estimation> |        |已认领|
|  88  | https://paperswithcode.com/paper/regularizing-and-optimizing-lstm-language |        |已认领|
|  89  | https://paperswithcode.com/paper/resnest-split-attention-networks | wanghy347 | 已领取 |
|  90  | https://paperswithcode.com/paper/retinamask-learning-to-predict-masks-improves |      |已认领|
|  91  | <https://paperswithcode.com/paper/searching-for-mobilenetv3> | 白槿  | 奖品已发放 |
|  92  | https://paperswithcode.com/paper/self-attention-generative-adversarial |        |已认领|
|  93  | https://paperswithcode.com/paper/self-critical-sequence-training-for-image |        |已认领|
|  94  | https://paperswithcode.com/paper/semi-supervised-classification-with-graph | 孙小北 | 奖品已发放     |
|  95  | https://paperswithcode.com/paper/sentence-bert-sentence-embeddings-using |        |已认领|
|  96  | https://paperswithcode.com/paper/sequence-to-sequence-learning-with-neural |        |已认领|
|  97  | https://paperswithcode.com/paper/show-and-tell-a-neural-image-caption |NicholasW|奖品已发放|
|  98  | <https://paperswithcode.com/paper/show-attend-and-tell-neural-image-caption> |        |已认领|
|  99  | https://paperswithcode.com/paper/simple-online-and-realtime-tracking |        |已认领 |
| 100  | https://paperswithcode.com/paper/simple-online-and-realtime-tracking-with-a |        |已认领|
| 101  | https://paperswithcode.com/paper/singan-learning-a-generative-model-from-a |周大山|已认领|
| 102  | https://paperswithcode.com/paper/soft-actor-critic-algorithms-and-applications |        |已认领|
| 103  | https://paperswithcode.com/paper/soft-actor-critic-off-policy-maximum-entropy |        |已认领|
| 104  | https://paperswithcode.com/paper/spectral-normalization-for-generative |        |已认领 |
| 105  | https://paperswithcode.com/paper/squeeze-and-excitation-networks |许杰 | 已领取 |
| 106  | https://paperswithcode.com/paper/ssd-single-shot-multibox-detector |        |已认领|
| 107  | https://paperswithcode.com/paper/stargan-unified-generative-adversarial |Negus|奖品已发放|
| 108  | https://paperswithcode.com/paper/towards-deep-learning-models-resistant-to |        |已认领|
| 109  | https://paperswithcode.com/paper/transfertransfo-a-transfer-learning-approach |        |已认领|
| 110  | https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical | MatthewQi | 奖品已发放 |
| 111  | <https://paperswithcode.com/paper/unpaired-image-to-image-translation-using> |        |已认领 |
| 112  | https://paperswithcode.com/paper/unsupervised-representation-learning-with-1 | 带带余除法| 已认领      |
| 113  | https://paperswithcode.com/paper/wasserstein-gan             | wwwwxl | 已领取 |
| 114  | https://paperswithcode.com/paper/weight-uncertainty-in-neural-networks |        |已认领 |
| 115  | https://paperswithcode.com/paper/yolact-better-real-time-instance-segmentation |        |已认领|
| 116  | <https://paperswithcode.com/paper/yolact-real-time-instance-segmentation> | dcx0001 | 已认领 |
| 117  | https://paperswithcode.com/paper/yolov3-an-incremental-improvement |    wqx    |   已认领   |
| 118  | <https://paperswithcode.com/paper/you-only-look-once-unified-real-time-object> |liwenlong2 | 已认领 |

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