However, entropy minimization-based methods force networks to agree on all parts of the training data. For extremely ambiguous regions, which are common in medical images, such agreement may be meaningless and unreliable. To this end, we present a conceptually simple yet effective method, termed ...
The concept has its origins in a 2006 paper titled“Model Compression.” Caruanaet alused what was a state-of-the-art classification model at the time, a hugeensemble modelcomprising of hundreds of base-level classifiers, to label a large data set, and then trained a single neural network ...
These three datasets have very different characteristics such as data collection sources and label tagging schemes. Thus, we regard that each of them is kept by a client to evaluate the performance of different federated learning methods in handling non-IID data. More details of the datasets and...
(TF-KD) which pre-trains the same student model as the teacher based on the label smoothing regularization finding. TF-FD further applies intra-layer and inter-layer distillations on the intermediate features to provide teacher-like knowledge [9]. However, feature knowledge and possible network ...
We first train a set of mod- els from scratch on the real dataset and record their expert training trajectories. We then initialize a new model with a random time step from a randomly chosen expert trajectory and train for several iterations on the synthetic dataset. Fi-...
First, it generates a "soft" label (a probability vector, not a cate- gorical label) that may not be straightforward to use when retraining the model. The training loss, for example, may need to be altered such that its compatible with soft labels. Second, for problems with structured ...
Recent work has shown data augmentation improves the generalization capability and robustness of the model, whether in original space or representation space, whether using label preservation or not. Despite its success, we have observed three limitations: The procedure is dataset-dependent, and thus ...
Semantic segmentation of oblique images aims to assign each image pixel with a unique label. It serves as the foundation of many urban applications, ranging from urban planning, intelligent transportation, dynamic monitoring to urban semantic 3D modeling (Sekeroglu and Tuncal, 2020, Li et al., ...
3. Firstly we set a binary mask M to separate the back- ground and foreground: \label {mask} M_{i,j}= \begin {cases} 1, & \text {if}\ \ (i,j)\in r \ 0, & \text {Otherwise} \end {cases} (2) 4645 where r denotes the ground-truth boxes and i, j are the...
Self-Teacher Network : Output Probability : Top-Down (Left) and Bottom-Up Path (Right) : Forward Path : Self-Feature Distillation : Soft Label Distillation : True Label Supervision Figure 2: Overview of our proposed self-knowledge distillation method, ...