Search Results for author: Sugiri Pranata

Found 9 papers, 5 papers with code

Equivariance and Invariance Inductive Bias for Learning from Insufficient Data

1 code implementation25 Jul 2022 Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang

We are interested in learning robust models from insufficient data, without the need for any externally pre-trained checkpoints.

Inductive Bias

Invariant Feature Regularization for Fair Face Recognition

2 code implementations ICCV 2023 Jiali Ma, Zhongqi Yue, Kagaya Tomoyuki, Suzuki Tomoki, Karlekar Jayashree, Sugiri Pranata, Hanwang Zhang

Unfortunately, face datasets inevitably capture the imbalanced demographic attributes that are ubiquitous in real-world observations, and the model learns biased feature that generalizes poorly in the minority group.

Face Recognition

A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion

1 code implementation3 Apr 2017 Lin Xiong, Jayashree Karlekar, Jian Zhao, Yi Cheng, Yan Xu, Jiashi Feng, Sugiri Pranata, ShengMei Shen

In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge.

Face Recognition Transfer Learning

Anomaly Detection with Adversarial Dual Autoencoders

2 code implementations arXiv.org 2019 Ha Son Vu, Daisuke Ueta, Kiyoshi Hashimoto, Kazuki Maeno, Sugiri Pranata, Sheng Mei Shen

Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently.

Anomaly Detection Image Generation

Person re-identification with fusion of hand-crafted and deep pose-based body region features

no code implementations27 Mar 2018 Jubin Johnson, Shunsuke Yasugi, Yoichi Sugino, Sugiri Pranata, ShengMei Shen

Person re-identification (re-ID) aims to accurately re- trieve a person from a large-scale database of images cap- tured across multiple cameras.

Metric Learning Person Re-Identification

RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents

no code implementations6 Feb 2024 Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar, Sugiri Pranata, Akira Kinose, Koki Oguri, Felix Wick, Yang You

Owing to recent advancements, Large Language Models (LLMs) can now be deployed as agents for increasingly complex decision-making applications in areas including robotics, gaming, and API integration.

Decision Making Retrieval

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