It is shown in a variety of image classification settings and on several datasets, that quasibinary classifiers are considerably better in classification settings where regular binary and softmax classifiers suffer, including zero-label and multi-label classification.
It is demonstrated that matrix multiplication can be reformulated as a series of convolutions by exploiting a special structure in the matrix using the Fast Fourier Trans-form (FFT), reducing the time complexity to O ( q 2 log q ) .
This paper studies what happens to the rest of the model when a class is forgotten, using a label-conditioned energy-based model (EBM) that assigns per-class energies, making the effect directly observable.
Syed Ali Ahmed, Syed Bilal Ahsan, Muhammad Zaigham Zaheer National University of Computer et al.· 0 citations
DiD is introduced, a label-free conversion method that exclusively trains the linear-attention backbone by aligning detector-facing interface tensors with those of a frozen Softmax teacher, and substantially outperforms established baselines and matches supervised, fully trained linear models.
Huaiyuan Qin, Gabriel James Goenawan, Zihang Lin et al.· 0 citations
Class-wise Covariance Regularization is proposed, which aligns the predicted covariance structure of class confidences with the semantic correlations encoded in pretrained text embed-dings with the geometric consistency of the class space throughout fine-tuning, resulting in more stable and interpretable confidence distributions across categories.
Ao Zhou, Zhiwei Jiang, Zifeng Cheng et al.· 0 citations
This thesis builds on an existing diagnostics toolkit mainly for t-SNE and UMAP and turns it into a more accessible package for interested practitioners, while also extending it with diagnostics tools.
Kasra Amirani, Sem Huisman, Dr. E.P.L. van Nieuwenburg· 0 citations
Visual classifiers are expected to generalize under data shifts, target shifts, and their combinations, yet most existing methods focus on domain invariance while failing to address intra-image predictive sufficiency. We investigate the structural hypothesis that each image contains a sample-adaptive oracle intra-image predictive subset sufficient for label prediction, while the remaining patches form non-essential complementary context that may correlate with the label. The theoretical analysis shows that restricting prediction to this oracle subset preserves the Bayes risk achievable by the full-patch representation while admitting a complexity bound that tightens with the oracle-subset size. Based on this view, we propose PatchGen, a text-free module that learns a sample-dependent soft predictive-subset mask as a task-driven proxy for the unobserved oracle subset mask. Specifically, histopathology visualizations suggest that PatchGen assigns higher scores to tumor-consistent regions than to some frequently co-occurring inflammatory context. Extensive experiments on natural and histopathological image benchmarks spanning all three shift settings show that PatchGen improves average performance over matched-backbone baselines in most evaluated configurations, enhances generalization to unknown classes, and remains competitive with vision-language methods without text supervision.