#contrastive learning
32 papers · 1 filter
Contrastive-Augmented Flow Matching for Style-Content Disentanglement
Yusong Li, Pingchuan Ma, Ming Gui +2
The paper proposes Contrastive Augmented Flow Matching (CAtFM), a method that adds contrastive regularization to invertible flow matching to learn disentangled content and style re…
DIVE: Embedding Compression via Self-Limiting Gradient Updates
Dongfang Zhao
The paper introduces DIVE, a residual compression adapter that reduces the dimensionality of language‑model embeddings using a self‑limiting hinge loss, geometry distillation, and…
Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis
Wenjing Liu, Qin Ren, Wen Zhang +2
The paper introduces TTA, a framework that first aligns shared patterns across histopathology images and genomic data and then preserves modality‑specific information to improve ca…
CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery
Jungho Oh, Woosung Kim, Dong Hyeon Mok +2
The paper introduces CatRetriever, a contrastive representation learning model that maps catalyst slab structures to their corresponding bulk crystals, enabling accurate retrieval…
The Devil Is in the Leakage: A Disentangled Dual-Purification Framework for High-Fidelity Hairstyle Transfer
Jijie Li, Jiankuo Zhao, Xiangyu Zhu +1
The paper introduces a Dual‑Purification Framework that reduces identity and geometric leakage in diffusion‑based hairstyle transfer, enabling high‑fidelity, identity‑preserving po…
SISA-Rec: A Semantically Integrated Sequential Recommender with Contrastive Alignment
Soohan Abbasi, Shahid Munir Shah, Rafia Shaikh +1
The paper introduces SISA-Rec, a transformer-based sequential recommender that combines item ID embeddings with BERT-derived text embeddings via gated fusion and contrastive alignm…