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Found 6 papers, 4 papers with code
A Novel Framework for Image-to-image Translation and Image Compression
Thus, we further propose a unified framework that allows both translation and autoencoding capabilities in a single codec.
3D Shapes Local Geometry Codes Learning with SDF
We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape.
Slimmable Compressive Autoencoders for Practical Neural Image Compression
Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance.
Semantic Drift Compensation for Class-Incremental Learning
The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes.
Learning Metrics from Teachers: Compact Networks for Image Embedding
In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks.
Weakly Supervised Domain-Specific Color Naming Based on Attention
The attention branch is used to modulate the pixel-wise color naming predictions of the network.
