https://github.com/gregoirepetit/fetril
공식 코드FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning
Papers with Code 아카이브
Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/greydanus/crypto-rnn
공식 코드https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation
공식 코드CoPE: Conditional image generation using Polynomial Expansions
https://github.com/gringham/bmx
공식 코드BMX: Boosting Natural Language Generation Metrics with Explainability
https://github.com/gringham/explainable-metrics-machine-translation
공식 코드Towards Explainable Evaluation Metrics for Natural Language Generation
https://github.com/gsarti/divemt
공식 코드DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
https://github.com/gsarti/labl
공식 코드https://github.com/gsarti/pecore
공식 코드Quantifying the Plausibility of Context Reliance in Neural Machine Translation
https://github.com/gsarti/qe4pe
공식 코드https://github.com/gsig/visual-grounding
공식 코드https://github.com/guangyliu/EISL
공식 코드Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
https://github.com/guojiapub/bique
공식 코드https://github.com/gustavostahl/cass
공식 코드CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
https://github.com/gwang-kim/diffusionclip
공식 코드DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation
https://github.com/h-aldarmaki/sent_translation_retrieval
공식 코드https://github.com/h-j-han/automatic_explicitation
공식 코드Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
https://github.com/h-j-han/speechqe
공식 코드SpeechQE: Estimating the Quality of Direct Speech Translation
https://github.com/h2oai/h2o-llmstudio
공식 코드https://github.com/ha0tang/landmarkgan
공식 코드https://github.com/haantran96/wavetransformer
공식 코드https://github.com/hainow/MCTN
공식 코드https://github.com/hamedhaghighi/cls2r
공식 코드A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
https://github.com/hamedhaghighi/coligen
공식 코드A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
https://github.com/hanhuang22/VI-Diff
공식 코드https://github.com/hannibal046/nmt_with_contrastive_memories
공식 코드Neural Machine Translation with Contrastive Translation Memories
https://github.com/hannibal046/scale
공식 코드SCALE: Synergized Collaboration of Asymmetric Language Translation Engines
https://github.com/haorannlp/mix
공식 코드https://github.com/happy-jihye/Cartoon-StyleGan2
공식 코드https://github.com/haroonshakeel/bilingual_sms_classification
공식 코드A Multi-cascaded Deep Model for Bilingual SMS Classification
https://github.com/haroonshakeel/multisenti
공식 코드Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text
https://github.com/harshvivek14/RoundTripOCR
공식 코드https://github.com/harvardnlp/BSO
공식 코드https://github.com/harvardnlp/cascaded-generation
공식 코드https://github.com/harvardnlp/nmt-android
공식 코드https://github.com/harvardnlp/seq2seq-attn
공식 코드https://github.com/harvardnlp/var-attn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.