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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tatHi/optok4at
공식 코드https://github.com/tathi/maxmatch_dropout
공식 코드https://github.com/tbepler/spatial-VAE
공식 코드Explicitly disentangling image content from translation and rotation with spatial-VAE
https://github.com/tdchaitanya/MMTOD
공식 코드Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
https://github.com/tech-srl/code2seq
공식 코드code2seq: Generating Sequences from Structured Representations of Code
https://github.com/tedyhabtegebrial/gvsnet
공식 코드Generative View Synthesis: From Single-view Semantics to Novel-view Images
https://github.com/teelinsan/parallel-decoding
공식 코드Accelerating Transformer Inference for Translation via Parallel Decoding
https://github.com/telepathylabsai/OpenDF
공식 코드https://github.com/temporarylora/temp-lora
공식 코드With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation
https://github.com/tempra28/nmtrnng
공식 코드Learning to Parse and Translate Improves Neural Machine Translation
https://github.com/tempra28/tree2seq
공식 코드https://github.com/temu-bsc/seq-to-seq-catalan
공식 코드https://github.com/tencent-ailab/ICML21_OAXE
공식 코드Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
https://github.com/tensorfieldnetworks/tensorfieldnetworks
공식 코드Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
https://github.com/tensorflow/gan/tree/master/tensorflow_gan/examples/dme_cyclegan
공식 코드Scientific Discovery by Generating Counterfactuals using Image Translation
https://github.com/tensorflow/lingvo
공식 코드The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
https://github.com/tensorflow/models
공식 코드https://github.com/tensorflow/tensor2tensor
공식 코드Depthwise Separable Convolutions for Neural Machine Translation
https://github.com/ternaus/robot-surgery-segmentation
공식 코드https://github.com/terryyz/rtt-rethinking
공식 코드Rethinking Round-Trip Translation for Machine Translation Evaluation
https://github.com/teslacool/SCA
공식 코드Soft Contextual Data Augmentation for Neural Machine Translation
https://github.com/textshuttle/exploiting-bias-to-debias
공식 코드Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model
https://github.com/thammegowda/005-nmt-imbalance
공식 코드Finding the Optimal Vocabulary Size for Neural Machine Translation
https://github.com/thammegowda/006-many-to-eng
공식 코드Many-to-English Machine Translation Tools, Data, and Pretrained Models
https://github.com/thammegowda/007-mt-eval-macro
공식 코드https://github.com/tharindudr/transQuest
공식 코드TransQuest: Translation Quality Estimation with Cross-lingual Transformers
https://github.com/thesouthfrog/stylealign
공식 코드Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Translation
https://github.com/thinkwee/DPP_CNN_Summarization
공식 코드https://github.com/thmoa/tex2shape
공식 코드Tex2Shape: Detailed Full Human Body Geometry From a Single Image
https://github.com/thompsonb/prism
공식 코드Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing
https://github.com/thu-coai/da-transformer
공식 코드Directed Acyclic Transformer for Non-Autoregressive Machine Translation
https://github.com/thu-keg/crab
공식 코드Constraint Back-translation Improves Complex Instruction Following of Large Language Models
https://github.com/thu-ml/diffusionbridge
공식 코드https://github.com/thumt/THUMT
공식 코드Improving the Transformer Translation Model with Document-Level Context
https://github.com/thunil/TecoGAN
공식 코드Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.