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Found 12 papers, 2 papers with code
PFB-Diff: Progressive Feature Blending Diffusion for Text-driven Image Editing
Diffusion models have showcased their remarkable capability to synthesize diverse and high-quality images, sparking interest in their application for real image editing.
Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch Representation
The cropped sketch patches are linked according to their global semantics or local geometric shapes, namely the synonymous proximity, by computing the cosine similarity between the captured patch embeddings.
IA-FaceS: A Bidirectional Method for Semantic Face Editing
To strike a balance between the reconstruction capacity and the control flexibility, the encoder is designed as a multi-head structure to yield embeddings for reconstruction and control, respectively: a high-dimensional tensor with spatial properties for consistent reconstruction and four low-dimensional facial component embeddings for semantic face editing.
Deep Rival Penalized Competitive Learning for Low-resolution Face Recognition
Current face recognition tasks are usually carried out on high-quality face images, but in reality, most face images are captured under unconstrained or poor conditions, e. g., by video surveillance.
IA-GM: A Deep Bidirectional Learning Method for Graph Matching
Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating the gradients of the matching loss.
Ranked #17 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Solve Traveling Salesman Problem by Monte Carlo Tree Search and Deep Neural Network
We present a self-learning approach that combines deep reinforcement learning and Monte Carlo tree search to solve the traveling salesman problem.
A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem
We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP).
Defense against Adversarial Examples by Encoder-Assisted Search in the Latent Coding Space
In the inference time, when given an input, we will start a search process in the latent space which aims to find the closest reconstruction to the given image on the distribution of normal data.
Revisit Lmser and its further development based on convolutional layers
Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing network, shortly Lmser, was a further development of the traditional auto-encoder (AE) by folding the architecture with respect to the central coding layer and thus leading to the features of symmetric weights and neurons, as well as jointly supervised and unsupervised learning.
A* Tree Search for Portfolio Management
By uniting the advantages in A* search algorithm with Monte Carlo tree search, we come up with a new algorithm named A* tree search in which best information is returned to guide next search.
Computational Decomposition of Style for Controllable and Enhanced Style Transfer
Neural style transfer has been demonstrated to be powerful in creating artistic image with help of Convolutional Neural Networks (CNN).
Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories
Moreover, the combination of CNN encoder and removal of KL-divergence, i. e., the sketch-pix2seq model, had better performance in learning and generating sketches of multiple categories and showed promising results in creativity tasks.
