Hugging Face Trending Papers
Gromov-Wasserstein Distillation for Inductive Multi-View Embedding
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arXiv Statistics ML
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Gromov-Wasserstein Distillation for Inductive Multi-View Embedding
arXiv:2609.40047v1 Announce Type: cross Abstract: Gromov-Wasserstein multidimensional scaling (GW-MDS) learns low-dimensional representations from relational data but remains transductive, providing...
arXiv Machine Learning
Aug 14
Multiview Representation Learning via Distributed Joint Latent Space Structuring
arXiv:2504. 18455v2 Announce Type: replace-cross Abstract: We study distributed multiview representation learning, a problem in which $K$ clients each observe a distinct but possibly statistically correlated view.
arXiv Computer Vision
Aug 31
Relational Knowledge Distillation Brings DNN Representations Close Enough to Humans to Be Aligned Without Supervision
The study investigates whether transferring relational structure from human mental representations to deep neural networks (DNNs) can improve fine‑grained alignment between the two. Using unsupervised Gromov‑Wasserstein optimal transport, the authors show that fine‑tuning pre‑trained DNNs with Relational Knowledge Distillation (RKD) brings the networks close enough to human representations to align at the individual‑object level on a test set of concepts not seen during training. The improvement is driven mainly by a more human‑like global structure of category distances, while local nearest‑neighbor overlap remains largely unchanged.
arXiv Computer Vision
Sep 11
CLIP-RD: Relational Distillation for Efficient CLIP Knowledge Distillation
CLIP-RD introduces a relational distillation framework for efficient CLIP knowledge distillation, featuring Vertical Relational Distillation (VRD) and Cross Relational Distillation (XRD). VRD aligns intra‑modal similarity distributions between teacher and student, while XRD aligns cross‑modal similarity distributions to enforce bidirectional symmetry. This joint modeling of multidirectional relational structures improves the student’s embedding geometry, yielding a 1.8%p performance gain over CLIP‑KD across various architectures, tasks, and corruption settings with minimal training‑time overhead.
arXiv AI
Aug 11
Three Necessary Principles for Self-Supervised Visual Representation Learning
arXiv:2608. 08309v1 Announce Type: cross Abstract: We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy.
arXiv Computer Vision
1d ago
GRAFT: Growing Agglomerative Foundation Models via Continual Teacher Distillation
arXiv:2610.02597v1 Announce Type: new Abstract: Vision foundation models such as DINOv2, SigLIP2, and MASt3R develop complementary capabilities from different pretraining objectives, yet their knowle...