Figure 1: An overview of Efficient Teacher framework. Efficient Teacher proposes three modules to implement a scalable and effective SSOD framework, where Dense Detector improves the quality of pseudo labels with dense input while has better inference efficiency; Pseudo Label Assigner divides pseudo lab...
Cox, Stephanie, Black, Jennifer, Heney, Jill, and Keith, Melissa. Promoting teacher presence strategies for effective and efficient feedback to student writing online. Teaching English in the Two-Year College, 42.4 (2015), 376-391.Cox, S., Black, J., Heney, J., Keith, M. (2015). ...
Tab. 7 shows the results with 1,000 training epochs and knowledge distillation using RegNetY-16GF [60] as the teacher model following [20] on ImageNet-1K [17] and ImageNet-ReaL [4]. Compared to LeViT-128S [20], EfficientViT-M4 sur- passes it by 0.5...
2019.02-The State of Sparsity in Deep Neural Networks 2021-TPAMI-Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks 2021-IJCV-Knowledge Distillation: A Survey 2020-Proceedings of the IEEE-Model Compression and Hardware Acceleration for Neural Networks:...
A study of the consequences of effective yet gradual learning on sensory representations must begin from a specific learning rule. One canonical learning rule is gradient descent, which proposes that neural updates improve behavior as much as possible for a given (very small) change in the overall...
Knowledge distillation (KD): It is worth mentioning that we use the same teacher model of DeiT [44] and Effi- cientFormer [20] in Table 2. We perform a baseline com- parison to show the efficiency of our method without us- ing KD....
teacher, to a more compact, computationally efficient one, known as the student. Despite the ability of such approaches to achieve comparable performance to the original model while significantly reducing computational demands, they may struggle to transfer the rationale behind the teacher’s decision ...
Overall, it can be concluded that the optimization algorithm was effective since it required only 30–40 iterations to optimize for 10 parameters. The total computation time required to obtain the optimized result was 2.5 h which includes both optimization and solver routines. The shapes and first...
First, in contrast to other methods relying on explicit su- pervision, e.g., using a pretrained teacher network to facili- tate the pruning [56, 78, 33], our token pruner is trained in an unsupervised manner; Note that no ground truth mask- ing is ...
Once the meaning has been clarified by the teacher, the solution rate might increase considerably. Change on this test might then reflect not only the test takers’ developing knowledge about the topic at hand but also their improved language skills. Closely related to the previous point, all ...