In Defense of LSTMs for Addressing Multiple Instance Learning Problems
LSTMs have a proven track record in analyzing sequential data. But what about unordered instance bags, as found under a Multiple Instance Learning (MIL) setting? While not often used for this, we show LSTMs excell under this setting too. In addition, we show thatLSTMs are capable of indirectly capturing instance-level information us-ing only bag-level annotations. Thus, they can be used to learn instance-level models in a weakly supervised manner. Our empirical evaluation on both simplified (MNIST) and realistic (Lookbook and Histopathology) datasets shows that LSTMs are competitive with or even surpass state-of-the-art methods specially designed for handling specific MIL problems. Moreover, we show that their performance on instance-level prediction is close to that of fully-supervised methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Multiple Instance LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs). Nevertheless, the robustness of these learning-based solvers has remained insufficient…
Reinforcement LearningAdversarial AttackCollaboration! Towards Robust Neural Methods for Routing Problems
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues -- their performance significantly deterior…
Out-of-Distribution GeneralizationWho is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs
Finding names of people killed by police has become increasingly important as police shootings get more and more public attention (police killing detection). Unfortunately, there has been not much work in the literature …
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based techniques for detecting intellectual p…
Graph Neural NetworkRed Teamingreinforcement-learningReinforcement Learning+1A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification
In recent years, long short-term memory neural networks (LSTMs) have been applied quite successfully to problems in handwritten text recognition. However, their strength is more located in handling sequences of variable …
Data AugmentationGeneral ClassificationHandwritten Text Recognition