DFKI
We use a frozen text encoder on image captions as a lightweight training-time auxiliary objective for image classifiers. The text components are drop.p.ed at inference, leaving a fast, unimodal vision model. Accuracy on ImageNet improves by up to +2.7 p.p. and downstream transfer by +1.0 p.p. on average, outperforming vision knowledge distillation at a fraction of the compute.
We fix Forward-Forward's slow inference by replacing per-class passes with a single forward pass through hyperspherical prototype matching. Thus, we achieve 40× faster inference with competitive accuracy.
We show that newer text-to-image models are progressively worse as training data generators, despite better visual quality, because they collapse to a narrow aesthetic-centric distribution that diverges from real data.
We introduce SubZeroCore, a novel, training-free coreset selection method that integrates submodular coverage and density into a single, unified objective.
We present HyperCore, a lightweight adaptive coreset selection framework designed for noisy environments. HyperCore utilizes per class hypersphere models and adaptively selects pruning thresholds.
We extend pretrained super-resolution models to larger images by using local-aware prompts.
We improve the training of vision transformers by segmenting and recombining objects and backgrounds from datasets. This makes the transformers more accurate, as well as more robust.
We conduct the first systematic study of dataset distillation for Super-Resolution.
We speed up diffusion classifiers by utilizing a label hierarchy and pruning unrelated paths.
We improve dataset distillation by distilling only a representative coreset.
We utilize the TaylorShift attention mechanism for global pixel-wise-attention in image super-resolution.