Search Results for author:
Found 7 papers, 4 papers with code
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories
For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs.
Learning Rare Category Classifiers on a Tight Labeling Budget
In this paper, we consider the scenario where we start with as-little-as five labeled positives of a rare category and a large amount of unlabeled data of which 99. 9% of it is negatives.
Background Splitting: Finding Rare Classes in a Sea of Background
We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories.
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Our goal is to enable machine learning systems to be trained interactively.
Learning to Move with Affordance Maps
In this paper, we combine the best of both worlds with a modular approach that learns a spatial representation of a scene that is trained to be effective when coupled with traditional geometric planners.
Online Model Distillation for Efficient Video Inference
Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning.
HydraNets: Specialized Dynamic Architectures for Efficient Inference
On ImageNet, applying the HydraNet template improves accuracy up to 2. 5% when compared to an efficient baseline architecture with similar inference cost.
