https://github.com/clovaai/dmfont
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Graph Matching is the problem of finding correspondences between two sets of vertices while preserving complex relational information among them. Since the graph structure has a strong capacity to represent objects and robustness to severe deformation and outliers, it is frequently adopted to formulate various correspondence problems in the field of computer vision. Theoretically, the Graph Matching problem can be solved by exhaustively searching the entire solution space. However, this approach is infeasible in practice because the solution space expands exponentially as the size of input data increases. For that reason, previous studies have attempted to solve the problem by using various approximation techniques.
Source: Consistent Multiple Graph Matching with Multi-layer Random Walks Synchronization
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Dynamic Graph Representation for Partially Occluded Biometrics
A Fast Projected Fixed-Point Algorithm for Large Graph Matching
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
MolGAN: An implicit generative model for small molecular graphs
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
Backpropagation through Combinatorial Algorithms: Identity with Projection Works
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Joint Graph Learning and Matching for Semantic Feature Correspondence
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
MolGAN: An implicit generative model for small molecular graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.