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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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평가표

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5

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hiyaroy12/multitask_learning

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/hjc18/language_modeling_lstm

Recurrent Neural Network Regularization

https://github.com/hkchengrex/TCN

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/hkuds/recdiff

Collaborative Translational Metric Learning

https://github.com/hlr/vln-trans

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

https://github.com/hmatty/stylized-neural-painting

Stylized Neural Painting

https://github.com/hnt4499/seq2seq

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/hoangtrungchinh/Bert-SQuAD-v2

Attention Is All You Need

https://github.com/hololee/YOLACT

YOLACT: Real-time Instance Segmentation

https://github.com/hongyi-zhang/Fixup

Fixup Initialization: Residual Learning Without Normalization

https://github.com/honzaMaly/cyclegan-style-transfer

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/hrbigelow/transformer-aiayn

Attention Is All You Need

https://github.com/hristina-uzunova/MEGAN

Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images

https://github.com/hrshtv/pytorch-lmu

Parallelizing Legendre Memory Unit Training

https://github.com/hsgodhia/hred

Mutual Information and Diverse Decoding Improve Neural Machine Translation

https://github.com/ht1221/leap-lstm

Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization

https://github.com/hthoai/UI2IT

Unsupervised Image-to-Image Translation Networks

https://github.com/huangxt39/BART_on_COVID_dialogue

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/huawei-noah/Pruning

Co-Evolutionary Compression for Unpaired Image Translation

https://github.com/hubiodatalab/druggen

Attention Is All You Need

https://github.com/huggingface/nn_pruning

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

https://github.com/huggingface/pytorch-image-models

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

https://github.com/huggingface/transformers

Attention Is All You Need

https://github.com/hughxiouge/CompoundE3D

Knowledge Graph Embedding with 3D Compound Geometric Transformations

https://github.com/hujinsen/pytorch-StarGAN-VC

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/hukuda222/code2seq

code2seq: Generating Sequences from Structured Representations of Code

https://github.com/husnain-ali21/image_captioning

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/huulinhcvp/chatBot

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/hwang1996/ACME

Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images

https://github.com/hwijeen/CPTG

Content preserving text generation with attribute controls

https://github.com/hwzwx1190522/gru

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

https://github.com/hx19940102/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/hyang0129/foodclassapp

Regularizing Neural Networks by Penalizing Confident Output Distributions

https://github.com/hytseng0509/DRIT_hr

Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/hyunahOh/instance_segmentation

YOLACT: Real-time Instance Segmentation

https://github.com/hyunwookl/testam

Attention Is All You Need

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