4 papers · 1 filter
Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation
Niclas Popp, Kevin Alexander Laube, Matthias Hein +1
Large foundation models trained on extensive datasets demonstrate strong zero-shot capabilities in various domains. Knowledge distillation has become an established tool for transf…
Single-Pass Object-Focused Data Selection
Niclas Popp, Dan Zhang, Jan Hendrik Metzen +2
While unlabeled image data is often plentiful, the costs of high-quality labels pose an important practical challenge: Which images should one select for labeling to use the annota…
Attention Is All You Need For Mixture-of-Depths Routing
Advait Gadhikar, Souptik Kumar Majumdar, Niclas Popp +3
Advancements in deep learning are driven by training models with increasingly larger numbers of parameters, which in turn heightens the computational demands. To address this issue…
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data
Leonhard Hennicke, Christian Medeiros Adriano, Holger Giese +2
Generative foundation models like Stable Diffusion comprise a diverse spectrum of knowledge in computer vision with the potential for transfer learning, e.g., via generating data t…