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Found 5 papers, 1 papers with code
Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling
This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling.
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.
Minutiae-Guided Fingerprint Embeddings via Vision Transformers
We further demonstrate that by guiding the ViT to focus in on local, minutiae related features, we can boost the recognition performance.
Identity Preserving Loss for Learned Image Compression
We present a novel Identity Preserving Reconstruction (IPR) loss function which achieves Bits-Per-Pixel (BPP) values that are ~38% and ~42% of CRF-23 HEVC compression for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets, respectively, while maintaining parity in recognition accuracy.
