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Appendix GOCor: BringingGloballyOptimized CorrespondenceVolumesintoYourNeuralNetwork
The Dynamic dataset allows the network to learn the presence of independently moving objects and motionboundaries.
DP-SSL: TowardsRobustSemi-supervisedLearning withAFewLabeledSamples
However, when the size of labeled data is very small (say a few labeled samples per class), SSL performs poorly and unstably, possibly due to the low qualityoflearnedpseudolabels.Inthispaper,weproposeanewSSLmethodcalled DP-SSL that adopts an innovative data programming (DP) scheme to generate probabilistic labels for unlabeled data. Different from existing DP methods that rely on human experts to provide initial labeling functions (LFs), we develop a multiple-choice learning (MCL) based approach to automatically generate LFs fromscratchinSSLstyle. Withthenoisylabelsproduced bytheLFs,wedesign a label model to resolve the conflict and overlap among the noisy labels, and finally infer probabilistic labels for unlabeled samples.
Appendix of A Deep Learning Dataloader with Shared Data Preparation
In this part, we show the I/O speed in the synchronous and asynchronous cases. Figure 3a show the I/O speed for four jobs that start at different moments. Then we further compare the RefCnt with the generic cache policy in the above cases. D = sample ([0, 13333], 10000) means sample a subset D with 10000 of size from [0, 13333] uniformly at random 36th Conference on Neural Information Processing Systems (NeurIPS 2022). DSA can always get the minimum misses.
Motif-basedGraphSelf-SupervisedLearningfor MolecularPropertyPrediction
Similar issues have also been encountered in natural language processing and computer vision.
Bench to Time lapseVideoGeneration
The emergence of large-scale text-to-image models [92, 60, 59, 58, 42, 5, 94, 14, 54, 40] has significantly advanced the field of Text-to-Video (T2V) generation [66,6,7,21,73,90]. Existing T2V architectures can be categorized into two types: U-Net-based and DiT-based. The latter focuses on recreating open-source structures similar to Sora [9], using the DiT (Diffusion-Transformer) [57]frameworkforT2Vgeneration [43,95,93,20]. When calculating theMTScore, thevideo retrievalmodel uses these texts toevaluate each frame ofthe video, assigning probabilities based on the matches. The final result is obtained by summing the general probability and the metamorphic probability.