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Found 5 papers, 3 papers with code
The CLEAR Benchmark: Continual LEArning on Real-World Imagery
The major strength of CLEAR over prior CL benchmarks is the smooth temporal evolution of visual concepts with real-world imagery, including both high-quality labeled data along with abundant unlabeled samples per time period for continual semi-supervised learning.
Streaming Self-Training via Domain-Agnostic Unlabeled Images
We present streaming self-training (SST) that aims to democratize the process of learning visual recognition models such that a non-expert user can define a new task depending on their needs via a few labeled examples and minimal domain knowledge.
QPyTorch: A Low-Precision Arithmetic Simulation Framework
Low-precision training reduces computational cost and produces efficient models.
Intermediate Level Adversarial Attack for Enhanced Transferability
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models.
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