- Computer Vision
- Natural Language Processing
- Vision-Language Models
- Speech Processing
- Reinforcement Learning
- Explainable AI
- Adversarial Attack
- Self-Supervised Learning
- Miscellaneous
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SPPNet(2014) [PDF]
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Faster R-CNN(2015) [PDF]
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YOLOv1(2016) [PDF]
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YOLOv2(2017) [PDF]
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CornerNet(2019) [PDF]
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CenterNet(2019) [PDF]
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U-Net(2015) [PDF]
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DeepLabV2(2016) [PDF]
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DeepLabV3(2017) [PDF]
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Mask R-CNN(2017) [PDF]
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DeepLabV3+(2018) [PDF]
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VAE(2013) [PDF]
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GAN(2014) [PDF]
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CGAN(2014) [PDF]
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DCGAN(2015) [PDF]
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Pix2Pix(2016) [PDF]
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PGGAN(2017) [PDF]
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CycleGAN(2017) [PDF]
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SytleGAN(2014) [PDF]
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DDPM(2020) [PDF]
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DDIM(2020) [PDF]
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C3D(2015) [PDF]
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S-CNN(2016) [PDF]
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TAD(2017) [PDF]
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RTD-Net(2021) [PDF]
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E2E-TAD(2022) [PDF]
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TADTR(2022) [PDF]
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ReAct(2022) [PDF]
- NeRF(2020) [PDF]
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LSTM(1997) [PDF]
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Bi-LSTM(1997) [PDF]
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Seq2Seq(2014) [PDF]
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GRU(2014) [PDF]
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Attention(2014) [PDF]
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Transforemr(2017) [PDF]
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BERT(2018) [PDF]
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GPT(2018) [PDF]
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Mamba(2023) [PDF]
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ALIGN(2021) [PDF]
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WaveNet(2016) [PDF]
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Tacotron(2017) [PDF]
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Tacotron2(2018) [PDF]
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FastSpeech(2019) [PDF]
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Transformer TTS(2019) [PDF]
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HiFi-GAN(2020) [PDF]
- DQN(2013) [PDF]
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FGSM(2015) [PDF]
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CW Attack(2017) [PDF]
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PGD Training(2017) [PDF]
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Adversarial Patch(2018) [PDF]
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BPDA(2018) [PDF]
- DNN for Youtube(2016) [PDF]