Paper Review

1.[paper-review] Sequence-to-Sequence Domain Adaptation Network for Robust Text Image Recognition

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2.[paper-review] Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations

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3.[paper-review] Generative adversarial networks with mixture of t-distributions noise for diverse image generation

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4.[paper-review] Unsupervised Data Augmentation for Consistency Training

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5.[paper-review] Generative Adversarial Nets

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6.[paper-review] Neural Machine Translation by Jointly Learning to Align and Translate

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7.[paper-review] You Only Look Once: Unified, Real-Time Object Detection

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8.[paper-review] Ultradense Word Embeddings by Orthogonal Transformation

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9.[paper-review] Attention Is All You Need

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10.[paper-review] SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network

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11.[paper-review] Self-Attention Generative Adversarial Networks

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12.[paper-review] Bottom-up Object Detection by Grouping Extreme and Center Points

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13.[paper-review] CornerNet: Detecting Objects as Paired Keypoints

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14.[paper-review] Objects as Points

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15.[paper-review] Page Segmentation using a Convolutional Neural Network with Trainable Co-occurrence Features

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16.[paper-review] Non-local Neural Networks

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17.[paper-review] Asymmetric Non-local Neural Networks for Semantic Segmentation

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18.[paper-review] GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

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19.[paper-review] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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20.[paper-review] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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21.[paper-review] Proxy Anchor Loss for Deep Metric Learning

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