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Current open-world semi-supervised learning methods fall short in practical applications because they fail to extract latent semantic information, but SECOS overcomes this by directly predicting textual labels from a candidate set, achieving state-of-the-art results.
Image compression, a seemingly benign process, can drastically amplify the power of adversarial attacks, making your image classifiers far more vulnerable than you thought.
