Search Results for author:
Found 6 papers, 2 papers with code
Distilling the Knowledge in Data Pruning
In this paper we explore the application of data pruning while incorporating knowledge distillation (KD) when training on a pruned subset.
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity preservation level, degree of appearance control, and low synthetic-to-real domain gap.
Asymmetric Image Retrieval with Cross Model Compatible Ensembles
In this setting, a large model is used for indexing the gallery while a lightweight model is used for querying.
Synthetic Data for Model Selection
In contrast to using synthetic data for training, in this work we explore whether synthetic data can be beneficial for model selection.
GAN-Control: Explicitly Controllable GANs
We present a framework for training GANs with explicit control over generated images.
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding
We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario.
