ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training.

Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy by forcing a new model to imitate the old models, or use ensembles, which multiply inference cost prohibitively. We analyze the role of ensembles in reducing NFR and observe that they remove negative flips that are typically not close to the decision boundary, but often exhibit large deviations in the distance among their logits.

Temporal Segment Networks for Action Recognition in Videos.

We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structure with a new segment-based sampling and aggregation scheme. This unique design enables the TSN framework to efficiently learn action models by using the whole video.

Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs.

Convolutional neural networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification.

New isoflavonolignan with quinone reductase inducing activity from Alhagi pseudalhagi (M.B.).

Bioassay-directed separation of the chloroform extracts from the air-dried aerial part of Alhagi pseudalhagi (M.B.) led to the isolation of a new isoflavonolignan (1), together with five known isoflavones (2-6) (Fig.