49 citations · 106 across the 14 of their papers we have counts for
15 papers
Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg +1
Mixup refers to interpolation-based data augmentation, originally motivated as a way to go beyond empirical risk minimization (ERM). Its extensions mostly focus on the definition o…
Towards Good Practices in Evaluating Transfer Adversarial Attacks
Zhengyu Zhao, Hanwei Zhang, Renjue Li +3
Transfer adversarial attacks raise critical security concerns in real-world, black-box scenarios. However, the actual progress of this field is difficult to assess due to two commo…
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis
Laurent Amsaleg, Oussama Chelly, Michael E. Houle +3
Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection,…
Teach me how to Interpolate a Myriad of Embeddings
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg +1
Mixup refers to interpolation-based data augmentation, originally motivated as a way to go beyond empirical risk minimization (ERM). Yet, its extensions focus on the definition of…
AggNet: Learning to Aggregate Faces for Group Membership Verification
Marzieh Gheisari, Javad Amirian, Teddy Furon +1
In some face recognition applications, we are interested to verify whether an individual is a member of a group, without revealing their identity. Some existing methods, propose a…
It Takes Two to Tango: Mixup for Deep Metric Learning
Shashanka Venkataramanan, Bill Psomas, Ewa Kijak +3
Metric learning involves learning a discriminative representation such that embeddings of similar classes are encouraged to be close, while embeddings of dissimilar classes are pus…
AlignMixup: Improving Representations By Interpolating Aligned Features
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg +1
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels. Many recent m…
Joint Learning of Assignment and Representation for Biometric Group Membership
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the iden…
Group Membership Verification with Privacy: Sparse or Dense?
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy f…
Walking on the Edge: Fast, Low-Distortion Adversarial Examples
Hanwei Zhang, Yannis Avrithis, Teddy Furon +1
Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural…
Privacy Preserving Group Membership Verification and Identification
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Similarly, gro…
Exquisitor: Interactive Learning at Large
Björn Þór Jónsson, Omar Shahbaz Khan, Hanna Ragnarsdóttir +6
Increasing scale is a dominant trend in today's multimedia collections, which especially impacts interactive applications. To facilitate interactive exploration of large multimedia…
Aggregation and Embedding for Group Membership Verification
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg +2
This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of quer…
Dynamicity and Durability in Scalable Visual Instance Search
Herwig Lejsek, Björn Þór Jónsson, Laurent Amsaleg +1
Visual instance search involves retrieving from a collection of images the ones that contain an instance of a visual query. Systems designed for visual instance search face the maj…
Searching in one billion vectors: re-rank with source coding
Hervé Jégou, Romain Tavenard, Matthijs Douze +1
Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that…